Awesome LLMs Datasets

Awesome-llms-Datasets

• Resuma os conjuntos de dados de texto Representative LLMS existentes em cinco dimensões: corpora pré-treinamento, conjuntos de dados de instruções de ajuste fino, conjuntos de dados de preferência, conjuntos de dados de avaliação e conjuntos de dados tradicionais de NLP . (Atualizações regulares)
• Novas seções de conjunto de dados foram adicionadas: conjuntos de dados de modelos de idiomas grandes multimodais (MLLMS), conjuntos de dados de geração aumentada de recuperação (RAG) . (Atualizações graduais)

Papel

O documento "conjuntos de dados para grandes modelos de idiomas: uma pesquisa abrangente" foi divulgada. （2024/2）

Resumo:

Este artigo embarca em uma exploração nos conjuntos de dados de Modelo de Linguagem Grande (LLM), que desempenham um papel crucial nos notáveis ​​avanços do LLMS. Os conjuntos de dados servem como infraestrutura fundamental análoga a um sistema radicular que sustenta e nutre o desenvolvimento do LLMS. Consequentemente, o exame desses conjuntos de dados surge como um tópico crítico na pesquisa. Para abordar a atual falta de uma visão geral abrangente e uma análise completa dos conjuntos de dados LLM e obter informações sobre seu status atual e tendências futuras, esta pesquisa consolida e categoriza os aspectos fundamentais dos conjuntos de dados LLM de cinco perspectivas: (1) pré- Corporação de treinamento; (2) Instruções de ajuste fino de conjunto de dados; (3) conjuntos de dados de preferência; (4) conjuntos de dados de avaliação; (5) conjuntos de dados tradicionais de processamento de linguagem natural (PNL). A pesquisa esclarece os desafios predominantes e aponta possíveis caminhos para investigação futura. Além disso, também é fornecida uma revisão abrangente dos recursos do conjunto de dados disponíveis existentes, incluindo estatísticas de 444 conjuntos de dados, cobrindo 8 categorias de idiomas e abrangendo 32 domínios. As informações de 20 dimensões são incorporadas às estatísticas do conjunto de dados. O tamanho total dos dados pesquisado ultrapassa 774,5 TB para corpora pré-treinamento e 700 milhões de instâncias para outros conjuntos de dados. Nosso objetivo é apresentar todo o cenário dos conjuntos de dados de texto LLM, servindo como uma referência abrangente para pesquisadores nesse campo e contribuindo para estudos futuros.

Fig 1. A arquitetura geral da pesquisa. Amplie o zoom para uma melhor visão

Módulo de informações do conjunto de dados

A seguir, é apresentado um resumo do módulo de informação do conjunto de dados.

• Nome do corpus/conjunto de dados
• Editor
• Tempo de liberação
  • "X" indica mês desconhecido.
• Tamanho
• Público ou não
  • "All" indica código aberto completo;
  • "Parcial" indica parcialmente código aberto;
  • "Não" indica não de código aberto.
• Licença
• Linguagem
  • "EN" indica inglês;
  • "ZH" indica chinês;
  • "AR" indica árabe;
  • "ES" indica espanhol;
  • "RU" indica russo;
  • "DE" indica alemão;
  • "Ko" indica coreano;
  • "LT" indica lituano;
  • "FA" indica persa/farsi;
  • "PL" indica a linguagem de programação;
  • "Multi" indica multilíngue, e o número entre parênteses indica o número de idiomas incluídos.
• Método de construção
  • "HG" indica corpus/conjunto de dados gerado por humanos;
  • "MC" indica o modelo construído de corpus/conjunto de dados;
  • "CI" indica coleta e melhoria do corpus/conjunto de dados existentes.
• Categoria
• Fonte
• Domínio
• Categoria de instrução
• Método de avaliação de preferência
  • "Vo" indica votação;
  • "Então" indica classificar;
  • "SC" indica a pontuação;
  • "-H" indica conduzido por humanos;
  • "-M" indica conduzido por modelos.
• Tipo de pergunta
  • "SQ" indica questões subjetivas;
  • "OQ" indica questões objetivas;
  • "Multi" indica vários tipos de perguntas.
• Método de avaliação
  • "CE" indica avaliação de código;
  • "Ele" indica avaliação humana;
  • "Eu" indica avaliação do modelo.
• Foco
• Números de categorias/subcategorias de avaliação
• Categoria de avaliação
• Número de categorias de entidades (tarefa ner)
• Número de categorias de relacionamento (RE Task)

Changelog

• （2024/01/17) Crie o repositório de dados Awesome-Llms-Datasets .
• （2024/02/02) Revise as informações para alguns conjuntos de dados; Adicione Dolma (corpora pré-treinamento | Corporadores gerais de pré-treinamento | Multi-Category).
• （2024/02/15) Adicione a coleção AYA (Instruções de ajuste fino dos conjuntos de dados de ajuste de instrução geral | HG & CI & MC); Conjunto de dados AYA (Instruções de ajuste fino de conjunto de dados | Instruções gerais de ajuste fino conjuntos de dados | HG).
• （2024/02/22) Adicione o OpenMathInstruct-1 (Instruções de ajuste fino dos conjuntos de dados de ajuste específico do domínio | FINBEN (DataSets de avaliação | Financeiro).
• （2024/04/05）
  • Adicione novas seções de conjunto de dados: (1) conjuntos de dados de modelos de idiomas grandes multimodais (MLLMS); (2) conjuntos de dados de geração aumentada de recuperação (RAG) .
  • Adicione MMRS-1M (DataSets MLLMS | Instruções de ajuste fino); VideoChat2-IT (DATASETS MLLMS | Instruções de ajuste fino); InstructDoc (MLLMS DATASETS | Instruções de ajuste fino); Dados allava-4V (DATASETS MLLMS | DATASETS DE AJUSTA FEM DE MLLMS); Mvbench (conjuntos de dados MLLMS | conjuntos de dados de avaliação); OlympiadBench (DataSets de MLLMS | DataSets de avaliação); MMMU (DataSets MLLMS | DataSets de avaliação).
  • Adicionar série de referência de pista (DataSets de avaliação | Plataforma de avaliação); RABOLA DE LIGADOR OPENLLM (DataSets de avaliação | Plataforma de avaliação); OpenCompass (DataSets de Avaliação | Plataforma de Avaliação); RABOLA DE LIGADO MTEB (DataSets de avaliação | Plataforma de avaliação); RABOLA DE LIGADOR C-MTEB (DataSets de avaliação | Plataforma de avaliação).
  • Adicione nah (agulha em um haystack) (conjuntos de dados de avaliação | texto longo); Tooleyes (DataSets de avaliação | ferramenta); Uhgeval (DataSets de avaliação | factualidade); Clongeval (DataSets de avaliação | Texto longo).
  • Adicione MathPile (Corpora pré-treinamento | Corporadores de pré-treinamento específicos de domínio | Matemática); WANJUAN-CC (corpora pré-treinamento | Páginas gerais de pré-treinamento |
  • Adicione IEPILE (Instruções de ajuste fino dos conjuntos de dados | Instruções gerais de ajuste fino conjuntos de dados | CI); Instructie (Instruções de ajuste fino dos conjuntos de dados | Instruções gerais de ajuste fino conjuntos de dados | HG).
  • Adicione CRUD-RAG (conjunto de dados RAG); Wikieval (conjuntos de dados de pano); RGB (conjuntos de dados RAG); Rag-Instruct-Benchmark-tester (conjuntos de dados de rag); ARES (conjuntos de dados de pano).
• （2024/04/06)
  • Adicionar GPQA (DataSets de Avaliação | Assunto); MGSM (DataSets de avaliação | multilíngue); Halueval-wild (conjunto de dados de avaliação | factualidade); Cmath (conjunto de dados de avaliação | sujeito); FineMath (DataSets de avaliação | Assunto); QA em tempo real (conjunto de dados de avaliação | factualidade); WYWEB (DataSets de avaliação | Assunto); ChinesefactEal (DataSets de avaliação | Factualidade); Estrelas de contagem (conjuntos de dados de avaliação | texto longo).
  • Adicione o chapéu de liquidação (corpora pré-treinamento | Corporadores gerais de pré-treinamento | Multi-Category); MassiveText (corpora pré-treinamento | Corporadores de pré-treinamento geral | Multi-Category); Madlad-400 (corpora pré-treinamento | corpora de pré-treinamento geral | Páginas da web); Minerva (corpora pré-treinamento | Corporadores de pré-treinamento geral | Multi-Category); CCALIGNED (Corpora pré-treinamento | Corpora de pré-treinamento geral | Corpus paralelo); Wikimatrix (corpora pré-treinamento | Corpora de pré-treinamento geral | Corpus paralelo); OpenWebmath (corporação pré-treinamento | Corporadores pré-treinamento específicos de domínio | Matemática).
  • Adicione WebQuestions (conjuntos de dados tradicionais de PNL | Responder a perguntas | QA do conhecimento).
  • Adicione a ALCE (conjuntos de dados RAG).
  • Adicionar alfafina (Instruções de ajuste fino dos conjuntos de dados | Instruções específicas de domínio conjuntos de dados de ajuste fino | Outros); CoIG-CQIA (Instruções de ajuste fino dos conjuntos de dados | Instruções gerais de ajuste fino conjuntos de dados | HG & CI).
• （2024/06/15)
  • Adicione pista (conjuntos de dados de avaliação | médico); CHC-banch (DataSets de avaliação | Geral); CIF-banch (conjunto de dados de avaliação | Geral); ACLUE (DataSets de avaliação | Assunto); LESC (DataSets de avaliação | NLU); Alignbench (DataSets de avaliação | MultitaSk); ScikNowEval (DataSets de avaliação | Assunto).
  • Adicione MAP-CC (corpora pré-treinamento | Geral pré-treinamento corporal | Multi-Category); FineWeb (corporativo pré-treinamento | corporativas gerais de pré-treinamento | web páginas); CCI 2.0 (corpora pré-treinamento | corporativas gerais de pré-treinamento |
  • Adicione WildChat (Instruções de ajuste fino de conjunto de dados | MC).
  • Adicione o OpenHerMesPReferences (conjuntos de dados de preferência | classificar); huozi_rlhf_data (conjuntos de dados de preferência | voto); Helpsteer (conjuntos de dados de preferência | pontuação); Helpsteer2 (conjuntos de dados de preferência | pontuação).
  • Adicionar banco MMT (DataSets MLLMS | DataSets de avaliação); Moscar (MLLMS DATASETS | PRÉ CORPORA PRÉ-TREINAMENTO); MM-NIAH (conjuntos de dados MLLMS | DataSets de avaliação).
  • Adicione CRAG (conjuntos de dados RAG).
• （2024/08/29）
  • Adicionar Gamebench (DataSets de Avaliação | Raciocínio); Halludial (conjuntos de dados de avaliação | factualidade); Wildbench (DataSets de Avaliação | Geral); Domaineval (DataSets de avaliação | código); Sysbench (DataSets de avaliação | Geral); KOBEST (DataSets de avaliação | NLU); Sarcasmbench (DataSets de avaliação | NLU); C ³ Bench (DataSets de Avaliação | Assunto); Tablebench (DataSets de Avaliação | Raciocínio); Arablegaleval (DataSets de avaliação | Law).
  • Adicionar multitrust (DataSets MLLMS | DataSets de avaliação); Obelisc (DataSets MLLMS | PRÉ CORPORA TREINAMENTO); Multimado (MLLMS DataSets | DataSets de avaliação).
  • Adicione DCLM (corpora pré-treinamento | Páginas gerais de pré-treinamento |
  • Adicione lituano-qa-v1 (conjunto de dados de ajuste fino de instrução | ci & mc); Reinscrutas (Instruções de ajuste fino dos conjuntos de dados | HG & CI & MC); Conversas Kollm (Instruções de ajuste fino de conjunto de dados | CI).
• （2024/09/04)
  • Adicione LongWriter-6K (Instruções de ajuste fino de conjunto de dados | CI & MC).
  • Adicione medtrinity-25m (conjuntos de dados MLLMS | DataSets de avaliação); MMIU (DATASETS MLLMS | DataSets de avaliação).
  • Adicione o Expository-Prose-V1 (Corpora pré-treinamento | Geral pré-treinamento corporal | Multi-Category).
  • Adicionar DebateQA (DataSets de Avaliação | Conhecimento); Bench Bench (DataSets de avaliação | Texto longo); Arabicmmlu (conjuntos de dados de avaliação | sujeito); Persianmmlu (DataSets de Avaliação | Assunto); Tmmlu+ (conjunto de dados de avaliação | Assunto).
  • Adicione RageVal (DataSets de RAG); LFRQA (conjuntos de dados RAG); MultiHop-RAG (conjuntos de dados RAG).
• Vamos liberar as informações do conjunto de dados no formato CSV.

Índice

• Corpora pré-treinamento
  • Corporadores gerais de pré-treinamento
    • Páginas da web
    • Textos de idioma
    • Livros
    • Materiais acadêmicos
    • Código
    • Corpus paralelo
    • Mídia social
    • Enciclopédia
    • Multi-categoria
  • Corpora pré-treinamento específica de domínio
    • Financeiro
    • Médico
    • Matemática
    • Outro
• Instruções de ajuste fino de conjunto de dados
  • Instrução Geral de ajuste fino de dados
    • Conjuntos de dados gerados por humanos (HG)
    • Modelo conjuntos de dados construídos (MC)
    • Coleção e melhoria dos conjuntos de dados existentes (IC)
    • HG & CI
    • HG & MC
    • CI & MC
    • HG & CI & MC
  • Instrução específica de domínio conjuntos de dados de ajuste fino
    • Médico
    • Código
    • Jurídico
    • Matemática
    • Educação
    • Outro
• Conjuntos de dados de preferência
  • Métodos de avaliação de preferência
    • Voto
    • Organizar
    • Pontuação
    • Outro
• Conjuntos de dados de avaliação
  • Em geral
  • Exame
  • Assunto
  • NLU
  • Raciocínio
  • Conhecimento
  • Texto longo
  • Ferramenta
  • Agente
  • Código
  • Ood
  • Lei
  • Médico
  • Financeiro
  • Normas sociais
  • Factualidade
  • Avaliação
  • Multitarefa
  • Multilíngue
  • Outro
  • Plataforma de avaliação
• Conjuntos de dados tradicionais de PNL
  • Resposta de perguntas
    • Compreensão de leitura
      • Seleção e julgamento
      • Teste de Cloze
      • Extração de resposta
      • QA irrestrito
    • QA do conhecimento
    • Raciocínio QA
  • Reconhecendo o implicação textual
  • Matemática
  • Resolução de Coreferência
  • Análise de sentimentos
  • Correspondência semântica
  • Geração de texto
  • Tradução de texto
  • Resumo do texto
  • Classificação de texto
  • Avaliação da qualidade do texto
  • Texto para código
  • Reconhecimento de entidade nomeado
  • Extração de relação
  • Multitarefa
• Conjuntos de dados multimodais de grandes modelos de linguagem (MLLMS)
  • Corpora pré-treinamento
  • Instruções de ajuste fino de conjunto de dados
  • Conjuntos de dados de avaliação
• Conjuntos de dados de geração aumentada (RAG) recuperados (RAG)

Corpora pré-treinamento

Os corpora pré-treinamento são grandes coleções de dados de texto usados ​​durante o processo de pré-treinamento do LLMS.

Corporadores gerais de pré-treinamento

Os corporais gerais de pré-treinamento são conjuntos de dados em larga escala compostos por texto extenso de diversos domínios e fontes. Sua principal característica é que o conteúdo do texto não se limita a um único domínio, tornando -os mais adequados para o treinamento de modelos fundamentais gerais. Os corpora são classificados com base em categorias de dados.

Formato de informações do conjunto de dados:

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Source:

Páginas da web

• CC-Stories 2018-6 | Não | En | Ci | Papel | Github | Conjunto de dados

  • Editor: Google Brain
  • Tamanho: 31 GB
  • Licença: -
  • Fonte: Crawl comum

• CC100 2020-7 | Tudo | Multi (100) | Ci | Papel | Conjunto de dados

  • Editor: Facebook AI
  • Tamanho: 2,5 TB
  • Licença: Termos de uso comuns de rastreamento
  • Fonte: Crawl comum

• ClueCorpus2020 2020-3 | Tudo | Zh | Ci | Papel | Conjunto de dados

  • Editora: Organização da Pista
  • Tamanho: 100 GB
  • Licença: MIT
  • Fonte: Crawl comum

• Crawl Common 2007-X | Tudo | Multi | Hg | Site

  • Editora: Common Crawl
  • Tamanho: -
  • Licença: Termos de uso comuns de rastreamento
  • Fonte: dados de rastreador da web

• Culturax 2023-9 | Tudo | Multi (167) | Ci | Papel | Conjunto de dados

  • Editor: Universidade de Oregon et al.
  • Tamanho: 27 TB
  • Licença: MC4 e licença do Oscar
  • Fonte: MC4, Oscar

• C4 2019-10 | Tudo | En | Ci | Papel | Conjunto de dados

  • Editor: Google Research
  • Tamanho: 12.68 TB
  • Licença: ODC-BY & Common Crawl Termos de uso
  • Fonte: Crawl comum

• MC4 2021-6 | Tudo | Multi (108) | Ci | Papel | Conjunto de dados

  • Editor: Google Research
  • Tamanho: 251 GB
  • Licença: ODC-BY & Common Crawl Termos de uso
  • Fonte: Crawl comum

• Oscar 22.01 2022-1 | Tudo | Multi (151) | Ci | Papel | Conjunto de dados | Site

  • Editora: Inria
  • Tamanho: 8.41 TB
  • Licença: CC0
  • Fonte: Crawl comum

• RealNews 2019-5 | Tudo | En | Ci | Papel | Github

  • Editora: Universidade de Washington et al.
  • Tamanho: 120 GB
  • Licença: Apache-2.0
  • Fonte: Crawl comum

• Redpajama-V2 2023-10 | Tudo | Multi (5) | Ci | Github | Conjunto de dados | Site

  • Editora: Together Computer
  • Tamanho: 30.4 T tokens
  • Licença: Termos de uso comuns de rastreamento
  • Fonte: Crawl Common, C4, etc.

• RefinedWeb 2023-6 | Parcial | En | Ci | Papel | Conjunto de dados

  • Editor: The Falcon LLM Team
  • Tamanho: 5000 GB
  • Licença: ODC-by-1.0
  • Fonte: Crawl comum

• WUDAOCORPORA-TEXT 2021-6 | Parcial | Zh | Hg | Papel | Conjunto de dados

  • Editor: Baai et al.
  • Tamanho: 200 GB
  • Licença: MIT
  • Fonte: páginas da web chinesas

• Wanjuan-CC 2024-2 | Parcial | En | Hg | Papel | Conjunto de dados

  • Editor: Laboratório de Inteligência Artifial de Xangai
  • Tamanho: 1 T tokens
  • Licença: CC-BY-4.0
  • Fonte: Crawl comum

• Madlad-400 2023-9 | Tudo | Multi (419) | Hg | Papel | Github | Conjunto de dados

  • Editor: Google Deepmind et al.
  • Tamanho: 2,8 T tokens
  • Licença: ODL-BY
  • Fonte: Crawl comum

• FineWeb 2024-4 | Tudo | En | Ci | Conjunto de dados

  • Editor: HuggingfaceFW
  • Tamanho: 15 tokens TB
  • Licença: ODC-by-1.0
  • Fonte: Crawl comum

• CCI 2.0 2024-4 | Tudo | Zh | Hg | DataSet1 | DataSET2

  • Editor: Baai
  • Tamanho: 501 GB
  • Licença: AGRANDO DE USO CCI
  • Fonte: páginas da web chinesas

• DCLM 2024-6 | Tudo | En | Ci | Papel | Github | Conjunto de dados | Site

  • Editora: Universidade de Washington et al.
  • Tamanho: 279,6 TB
  • Licença: Termos de uso comuns de rastreamento
  • Fonte: Crawl comum

Textos de idioma

• ANC 2003-X | Tudo | En | Hg | Site

  • Editora: The US National Science Foundation et al.
  • Tamanho: -
  • Licença: -
  • Fonte: textos em inglês americano

• BNC 1994-X | Tudo | En | Hg | Site

  • Editor: Oxford University Press et al.
  • Tamanho: 4124 textos
  • Licença: -
  • Fonte: textos ingleses britânicos

• Notícias-crawl 2019-1 | Tudo | Multi (59) | Hg | Conjunto de dados

  • Editor: Ukri et al.
  • Tamanho: 110 GB
  • Licença: CC0
  • Fonte: jornais

Livros

• Arquivo de Anna 2023-X | Tudo | Multi | Hg | Site

  • Editora: Anna
  • Tamanho: 586,3 TB
  • Licença: -
  • Fonte: Sci-Hub, Gênesis da Biblioteca, Library Z, etc.

• BookcorpusOpen 2021-5 | Tudo | En | Ci | Papel | Github | Conjunto de dados

  • Editor: Jack Bandy et al.
  • Tamanho: 17.868 livros
  • Licença: Smashwords Termos de Serviço
  • Fonte: Toronto Book Corpus

• PG-19 2019-11 | Tudo | En | Hg | Papel | Github | Conjunto de dados

  • Editor: Deepmind
  • Tamanho: 11,74 GB
  • Licença: Apache-2.0
  • Fonte: Projeto Gutenberg

• Projeto Gutenberg 1971-X | Tudo | Multi | Hg | Site

  • Editor: Ibiblio et al.
  • Tamanho: -
  • Licença: o projeto Gutenberg
  • Fonte: Dados do eBook

• Smashwords 2008-x | Tudo | Multi | Hg | Site

  • Editor: Draft2digital et al.
  • Tamanho: -
  • Licença: Smashwords Termos de Serviço
  • Fonte: Dados do eBook

• Toronto Book Corpus 2015-6 | Não | En | Hg | Papel | Site

  • Editora: Universidade de Toronto et al.
  • Tamanho: 11.038 livros
  • Licença: MIT e Smashwords Termos de Serviço
  • Fonte: Smashwords

Materiais acadêmicos

• Arxiv 1991-x | Tudo | En | Hg | Site

  • Editor: Paul Ginsparg et al.
  • Tamanho: -
  • Licença: Termos de Uso para APIs ARXIV
  • Fonte: ARXIV pré -impressão

• S2ORC 2020-6 | Tudo | En | Ci | Papel | Github

  • Editora: AI2 et al.
  • Tamanho: 81,1 MB
  • Licença: ODC-by-1.0
  • Fonte: Mag, Arxiv, PubMed, etc.

Código

• BigQuery 2022-3 | Não | PL | Ci | Papel | Github

  • Editor: Salesforce Research
  • Tamanho: 341.1 GB
  • Licença: Apache-2.0
  • Fonte: BigQuery

• Github 2008-4 | Tudo | PL | Hg | Site

  • Editor: Microsoft
  • Tamanho: -
  • Licença: -
  • Fonte: vários projetos de código

• phi-1 2023-6 | Não | En & pl | HG & MC | Papel | Conjunto de dados

  • Editor: Microsoft Research
  • Tamanho: 7 T tokens
  • Licença: CC-BY-NC-SA-3.0
  • Fonte: The Stack, Stackoverflow, GPT-3.5 Geração

• A pilha 2022-11 | Tudo | PL (358) | Hg | Papel | Conjunto de dados

  • Editor: ServiceNow Research et al.
  • Tamanho: 6 TB
  • Licença: os termos das licenças originais
  • Fonte: Arquivos de código fonte de licenciamento permissivamente

Corpus paralelo

• MTP 2023-9 | Tudo | En & zh | HG & CI | Conjunto de dados

  • Editor: Baai
  • Tamanho: 1,3 TB
  • Licença: Protocolo de uso de dados BAAI
  • Fonte: pares de texto paralelos chineses-inglês na web

• Multiun 2010-5 | Tudo | Multi (7) | Hg | Papel | Site

  • Editora: Centro de Pesquisa Alemã de Inteligência Artificial (DFKI) GmbH
  • Tamanho: 4353 MB
  • Licença: -
  • Fonte: Documentos das Nações Unidas

• Paracrawl 2020-7 | Tudo | Multi (42) | Hg | Papel | Site

  • Editor: Prompsit et al.
  • Tamanho: 59996 arquivos
  • Licença: CC0
  • Fonte: dados de rastreador da web

• UNCORPUS V1.0 2016-5 | Tudo | Multi (6) | Hg | Papel | Site

  • Editora: Nações Unidas et al.
  • Tamanho: arquivos 799276
  • Licença: -
  • Fonte: Documentos das Nações Unidas

• CCALIGNED 2020-11 | Tudo | Multi (138) | Hg | Papel | Conjunto de dados

  • Editor: Facebook AI et al.
  • Tamanho: pares de URL de 392 M
  • Licença: -
  • Fonte: Crawl comum

• Wikimatrix 2021-4 | Tudo | Multi (85) | Hg | Papel | Github | Conjunto de dados

  • Editor: Facebook AI et al.
  • Tamanho: 134 m sentenças paralelas
  • Licença: CC-BY-SA
  • Fonte: Wikipedia

Mídia social

• OpenWebText 2019-4 | Tudo | En | Hg | Site

  • Editora: Brown University
  • Tamanho: 38 GB
  • Licença: CC0
  • Fonte: Reddit

• Reddit 2020-1 | Tudo | En | Ci | Papel | Site

  • Editora: PushShift.io et al.
  • Tamanho: 2 TB
  • Licença: -
  • Fonte: Reddit

• Reddit 2005-6 | Tudo | En | Hg | Site

  • Editor: Condé Nast Digital et al.
  • Tamanho: -
  • Licença: -
  • Fonte: Postagens de mídia social

• Stackexchange 2008-9 | Tudo | En | Hg | Conjunto de dados | Site

  • Editora: Stack Exchange
  • Tamanho: -
  • Licença: CC-BY-SA-4.0
  • Fonte: Dados da pergunta e resposta da comunidade

• WebText 2019-2 | Parcial | En | Hg | Papel | Github | Conjunto de dados

  • Editora: Openai
  • Tamanho: 40 GB
  • Licença: MIT
  • Fonte: Reddit

• Zhihu 2011-1 | Tudo | Zh | Hg | Site

  • Editor: Pequim Zhizhe Tianxia Technology Co., Ltd
  • Tamanho: -
  • Licença: Contrato de Usuário Zhihu
  • Fonte: Postagens de mídia social

Enciclopédia

• Baidu Baike 2008-4 | Tudo | Zh | Hg | Site

  • Editor: Baidu
  • Tamanho: -
  • Licença: Contrato de Usuário de Baidu Baike
  • Fonte: Dados de conteúdo enciclopédico

• Tigerbot-wiki 2023-5 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 205 MB
  • Licença: Apache-2.0
  • Fonte: Baidu Baike

• Wikipedia 2001-1 | Tudo | Multi | Hg | Conjunto de dados | Site

  • Editora: Wikimedia Foundation
  • Tamanho: -
  • Licença: CC-BY-SA-3.0 & GFDL
  • Fonte: Dados de conteúdo enciclopédico

Multi-categoria

• Arabictext 2022 2022-12 | Tudo | Ar | HG & CI | Conjunto de dados

  • Editor: Baai et al.
  • Tamanho: 201.9 GB
  • Licença: CC-BY-SA-4.0
  • Fonte: Arábico, Oscar, CC100, etc.

• MNBVC 2023-1 | Tudo | Zh | HG & CI | Github | Conjunto de dados

  • Editor: Comunidade Liwu
  • Tamanho: 20811 GB
  • Licença: MIT
  • Fonte: Livros chineses, páginas da web, teses, etc.

• Redpajama-V1 2023-4 | Tudo | Multi | HG & CI | Github | Conjunto de dados

  • Editora: Together Computer
  • Tamanho: 1,2 T tokens
  • Licença: -
  • Fonte: rastreamento comum, github, livros, etc.

• Raízes 2023-3 | Parcial | Multi (59) | HG & CI | Papel | Conjunto de dados

  • Editora: Hugging Face et al.
  • Tamanho: 1,61 TB
  • Licença: Bloom-Rail-M
  • Fonte: Oscar, Github, etc.

• A pilha 2021-1 | Tudo | En | HG & CI | Papel | Github | Conjunto de dados

  • Editor: Eleutherai
  • Tamanho: 825.18 GB
  • Licença: MIT
  • Fonte: Livros, Arxiv, Github, etc.

• Tigerbot_pretrain_en 2023-5 | Parcial | En | Ci | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 51 GB
  • Licença: Apache-2.0
  • Fonte: livros em inglês, páginas da web, en-wiki, etc

• Tigerbot_pretrain_zh 2023-5 | Parcial | Zh | Hg | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 55 GB
  • Licença: Apache-2.0
  • Fonte: livros chineses, páginas da web, zh-wiki, etc.

• WanjuantExt-1.0 2023-8 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editora: Shanghai Ai Laboratory
  • Tamanho: 1094 GB
  • Licença: CC-BY-4.0
  • Fonte: Páginas da Web, Enciclopédia, Livros, etc

• Dolma 2024-1 | Tudo | En | HG & CI | Papel | Github | Conjunto de dados

  • Editora: AI2 et al.
  • Tamanho: 11519 GB
  • Licença: MR Contrato
  • Fonte: Projeto Gutenberg, C4, Reddit, etc.

• Vlipajama 2023-6 | Tudo | En | HG & CI | Github | Conjunto de dados | Site

  • Editor: Cerebras et al.
  • Tamanho: 627 B Tokens
  • Licença: -
  • Fonte: Crawl Common, C4, Github, etc.

• MassiveText 2021-12 | Não | Multi | HG & CI | Papel

  • Editor: Google DeepMind
  • Tamanho: 10,5 TB
  • Licença: -
  • Fonte: MassiveWeb, C4, livros, etc.

• Minerva 2022-6 | Não | En | Hg | Papel

  • Editor: Google Research
  • Tamanho: 38,5 B Tokens
  • Licença: -
  • Fonte: Arxiv, páginas da web, etc.

• MAP-CC 2024-4 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados | Site

  • Editora: Comunidade de Pesquisa de Projeção de Arte Multimodal et al.
  • Tamanho: 840,48 B Token
  • Licença: CC-BY-NC-ND-4.0
  • Fonte: Rastreamento comum chinês, enciclopédias chinesas, livros chineses etc.

• Expository-Prose-V1 2024-8 | Tudo | En | HG & CI | Papel | Github | Conjunto de dados

  • Editor: Pints.ai Labs
  • Tamanho: 56 B Tokens
  • Licença: MIT
  • Fonte: Arxiv, Wikipedia, Gutenberg, etc.

Corpora pré-treinamento específica de domínio

Os corpora pré-treinamento específicos de domínio são conjuntos de dados LLM personalizados para campos ou tópicos específicos. O tipo de corpus é normalmente empregado na fase de pré-treinamento incremental do LLMS. Os corpora são classificados com base nos domínios de dados.

Formato de informações do conjunto de dados:

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Source:
  - Category:
  - Domain:

Financeiro

• BBT-Fincorpus 2023-2 | Parcial | Zh | Hg | Papel | Github | Site

  • Editor: Fudan University et al.
  • Tamanho: 256 GB
  • Licença: -
  • Fonte: anúncios da empresa, relatórios de pesquisa, financeiro
  • Categoria: Multi
  • Domínio: Finanças

• FinCorpus 2023-9 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editora: Du Xiaoman
  • Tamanho: 60,36 GB
  • Licença: Apache-2.0
  • Fonte: anúncios da empresa, notícias financeiras, questões de exames financeiros
  • Categoria: Multi
  • Domínio: Finanças

• Finglm 2023-7 | Tudo | Zh | Hg | Github

  • Editor: Conhecimento Atlas et al.
  • Tamanho: 69 GB
  • Licença: Apache-2.0
  • Fonte: Relatórios anuais de empresas listadas
  • Categoria: textos de idioma
  • Domínio: Finanças

• Tigerbot-Earning 2023-5 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 488 MB
  • Licença: Apache-2.0
  • Fonte: Relatórios Financeiros
  • Categoria: textos de idioma
  • Domínio: Finanças

• Tigerbot-Research 2023-5 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 696 MB
  • Licença: Apache-2.0
  • Fonte: Relatórios de pesquisa
  • Categoria: textos de idioma
  • Domínio: Finanças

Médico

• Medical-PT 2023-5 | Tudo | Zh | Ci | Github | Conjunto de dados

  • Editor: Ming Xu
  • Tamanho: 632,78 MB
  • Licença: Apache-2.0
  • Fonte: Dados da Enciclopédia Médica, Livros Médicos
  • Categoria: Multi
  • Domínio: Médico

• PubMed Central 2000-2 | Tudo | En | Hg | Site

  • Editor: NCBI
  • Tamanho: -
  • Licença: Aviso de direitos autorais do PMC
  • Fonte: Literatura Científica Biomédica
  • Categoria: Materiais Acadêmicos
  • Domínio: Médico

Matemática

• Prova-PILLE-2 2023-10 | Tudo | En | HG & CI | Papel | Github | Conjunto de dados | Site

  • Editor: Universidade de Princeton et al.
  • Tamanho: 55 B Tokens
  • Licença: -
  • Fonte: Arxiv, OpenWebmath, AlgebraicStack
  • Categoria: Multi
  • Domínio: Matemática

• MATHPILE 2023-12 | Tudo | En | Hg | Papel | Github | Conjunto de dados

  • Editora: Shanghai Jiao Tong University et al.
  • Tamanho: 9,5 B Tokens
  • Licença: CC-BY-NC-SA-4.0
  • Fonte: livros didáticos, Wikipedia, Proofwiki, CommonCrawl, Stackexchange, Arxiv
  • Categoria: Multi
  • Domínio: Matemática

• OpenWebMath 2023-10 | Tudo | En | Hg | Papel | Github | Conjunto de dados

  • Editora: Universidade de Toronto et al.
  • Tamanho: 14,7 B Tokens
  • Licença: ODC-by-1.0
  • Fonte: Crawl comum
  • Categoria: Páginas da Web
  • Domínio: Matemática

Outro

• Tigerbot-Law 2023-5 | Tudo | Zh | Hg | Papel | Github | Conjunto de dados

  • Editor: Tigerbot
  • Tamanho: 29,9 MB
  • Licença: Apache-2.0
  • Fonte: Cláusulas legais
  • Categoria: textos de idioma
  • Domínio: Lei

• Transgpt-pt 2023-7 | Tudo | Zh | Hg | Github | Conjunto de dados

  • Editora: Universidade de Pequim Jiaotong
  • Tamanho: 35,8 MB
  • Licença: Apache-2.0
  • Fonte: documentos de tecnologia, informações de construção de engenharia, dados estatísticos, etc.
  • Categoria: Multi
  • Domínio: transporte

Instruções de ajuste fino de conjunto de dados

Os conjuntos de dados de ajuste fino de instrução consistem em uma série de pares de texto que compreendem "Instrução de insumos" e "Saídas de resposta". “Instrução Instruções” representam solicitações feitas pelos humanos ao modelo. Existem vários tipos de instruções, como classificação, resumo, parafraseando, etc. “Saídas de resposta” são as respostas geradas pelo modelo após a instrução e o alinhamento com as expectativas humanas.

Instrução Geral de ajuste fino de dados

Os conjuntos de dados de ajuste fina de instruções gerais contêm uma ou mais categorias de instruções sem restrições de domínio, com o objetivo principalmente de aprimorar a capacidade de acompanhar as instruções do LLMS em tarefas gerais. Os conjuntos de dados são classificados com base em métodos de construção.

Formato de informações do conjunto de dados:

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Source:
  - Instruction Category:

Conjuntos de dados gerados por humanos (HG)

• Databricks-Dolly-15k 2023-4 | Tudo | En | Hg | Conjunto de dados | Site

  • Editor: Databricks
  • Tamanho: 15011 instâncias
  • Licença: CC-BY-SA-3.0
  • Fonte: gerado manualmente com base em diferentes categorias de instruções
  • Categoria de instrução: multi

• Instructionwild_v2 2023-6 | Tudo | En & zh | Hg | Github

  • Editor: Universidade Nacional de Cingapura
  • Tamanho: 110k instâncias
  • Licença: -
  • Fonte: coletado na web
  • Categoria de instrução: multi

• LCCC 2020-8 | Tudo | Zh | Hg | Papel | Github

  • Editor: Tsinghua University et al.
  • Tamanho: 12m Instâncias
  • Licença: MIT
  • Fonte: Rastrear interações do usuário nas mídias sociais
  • Categoria de instrução: multi

• OASST1 2023-4 | Tudo | Multi (35) | Hg | Papel | Github | Conjunto de dados

  • Editor: OpenAssistant
  • Tamanho: 161443 Instâncias
  • Licença: Apache-2.0
  • Fonte: gerado e anotado por humanos
  • Categoria de instrução: multi

• OL-CC 2023-6 | Tudo | Zh | Hg | Conjunto de dados

  • Editor: Baai
  • Tamanho: 11655 instâncias
  • Licença: Apache-2.0
  • Fonte: gerado e anotado por humanos
  • Categoria de instrução: multi

• Zhihu-kol 2023-3 | Tudo | Zh | Hg | Github | Conjunto de dados

  • Editora: Wangrui6
  • Tamanho: 1006218 Instâncias
  • Licença: MIT
  • Fonte: Rastreie de Zhihu
  • Categoria de instrução: multi

• DataSet AYA 2024-2 | Tudo | Multi (65) | Hg | Papel | Conjunto de dados | Site

  • Editor: Coere para a Comunidade de AI et al.
  • Tamanho: 204k instâncias
  • Licença: Apache-2.0
  • Fonte: coletado manualmente e anotado através da plataforma de anotação AYA
  • Categoria de instrução: multi

• Instructie 2023-5 | Tudo | En & zh | Hg | Papel | Github | Conjunto de dados

  • Editora: Zhejiang University et al.
  • Tamanho: 371700 instâncias
  • Licença: MIT
  • Fonte: Baidu Baike, Wikipedia
  • Categoria de instrução: extração

Modelo conjuntos de dados construídos (MC)

• ALPACA_DATA 2023-3 | Tudo | En | Mc | Github

  • Editor: Stanford Alpaca
  • Tamanho: 52k instâncias
  • Licença: Apache-2.0
  • Fonte: gerado por text-davinci-003 com avós de APLACA_DATA
  • Categoria de instrução: multi

• Belle_generated_chat 2023-5 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 396004 Instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por chatgpt
  • Categoria de instrução: geração

• Belle_multiturn_chat 2023-5 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 831036 Instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por chatgpt
  • Categoria de instrução: multi

• Belle_train_0.5m_cn 2023-4 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 519255 Instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por text-davinci-003
  • Categoria de instrução: multi

• Belle_train_1m_cn 2023-4 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 917424 Instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por text-davinci-003
  • Categoria de instrução: multi

• Belle_train_2m_cn 2023-5 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 2M instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por chatgpt
  • Categoria de instrução: multi

• Belle_train_3.5m_cn 2023-5 | Tudo | Zh | Mc | Github | Conjunto de dados

  • Editor: Belle
  • Tamanho: 3606402 Instâncias
  • Licença: GPL-3.0
  • Fonte: gerado por chatgpt
  • Categoria de instrução: multi

• Camelo 2023-3 | Tudo | Multi & PL | Mc | Papel | Github | Conjunto de dados | Site

  • Editor: Kaust
  • Tamanho: 1659328 Instâncias
  • Licença: CC-BY-NC-4.0
  • Fonte: Diálogo gerado por dois agentes GPT-3.5-Turbo
  • Categoria de instrução: multi

• Chatgpt_corpus 2023-6 | Tudo | Zh | Mc | Github

  • Editor: Plexpt
  • Tamanho: 3270k instâncias
  • Licença: GPL-3.0
  • Fonte: gerado pelo GPT-3.5-Turbo
  • Categoria de instrução: multi

• Instructionwild_v1 2023-3 | Tudo | En & zh | Mc | Github

  • Editor: Universidade Nacional de Cingapura
  • Tamanho: 104k instâncias
  • Licença: -
  • Fonte: gerado pela API OpenAi
  • Categoria de instrução: multi

• LMSYS-ChAT-1M 2023-9 | Tudo | Multi | Mc | Papel | Conjunto de dados

  • Editor: UC Berkeley et al.
  • Tamanho: 1M Instâncias
  • Licença: Licença LMSYS-ChAT-1M
  • Fonte: gerado por vários LLMs
  • Categoria de instrução: multi

• Moss_002_sft_data 2023-4 | Tudo | En & zh | Mc | Github | Conjunto de dados

  • Editora: Universidade Fudan
  • Tamanho: 1161137 Instâncias
  • Licença: CC-BY-NC-4.0
  • Fonte: gerado por text-davinci-003
  • Categoria de instrução: multi

• Moss_003_sft_data 2023-4 | Tudo | En & zh | Mc | Github | Conjunto de dados

  • Editora: Universidade Fudan
  • Tamanho: 1074551 Instâncias
  • Licença: CC-BY-NC-4.0
  • Fonte: Dados de conversa de Moss-002 e gerados pelo GPT-3.5-Turbo
  • Categoria de instrução: multi

• Moss_003_sft_plugin_data 2023-4 | Parcial | En & zh | Mc | Github | Conjunto de dados

  • Editora: Universidade Fudan
  • Tamanho: instâncias 300k
  • Licença: CC-BY-NC-4.0
  • Fonte: gerado por plugins e LLMS
  • Categoria de instrução: multi

• OpenChat 2023-7 | Tudo | En | Mc | Papel | Github | Conjunto de dados

  • Editor: Tsinghua University et al.
  • Tamanho: 70 mil instâncias
  • Licença: MIT
  • Fonte: ShareGpt
  • Categoria de instrução: multi

• Redgpt-DataSet-V1-CN 2023-4 | Parcial | Zh | Mc | Github

  • Editor: Da-Southampton
  • Tamanho: 50k instâncias
  • Licença: Apache-2.0
  • Fonte: gerado pelo LLMS
  • Categoria de instrução: multi

• Auto-instrução 2022-12 | Tudo | En | Mc | Papel | Github

  • Editora: Universidade de Washington et al.
  • Tamanho: 52445 instâncias
  • Licença: Apache-2.0
  • Fonte: gerado pelo GPT-3
  • Categoria de instrução: multi

• Sharechat 2023-4 | Tudo | Multi | Mc | Site

  • Editor: Sharechat
  • Tamanho: Instâncias 90k
  • Licença: CC0
  • Fonte: ShareGpt
  • Categoria de instrução: multi

• Sharegpt-Chinese-Inglish-90K 2023-7 | Tudo | En & zh | Mc | Github | Conjunto de dados

  • Editor: Sharei
  • Tamanho: Instâncias 90k
  • Licença: Apache-2.0
  • Fonte: ShareGpt
  • Categoria de instrução: multi

• ShareGPT90K 2023-4 | Tudo | En | Mc | Conjunto de dados

  • Editor: Ryokoai
  • Tamanho: Instâncias 90k
  • Licença: CC0
  • Fonte: ShareGpt
  • Categoria de instrução: multi

• Ultrachat 2023-5 | Tudo | En | Mc | Papel | Github

  • Editor: Universidade de Tsinghua
  • Tamanho: 1468352 Instâncias
  • Licença: CC-BY-NC-4.0
  • Fonte: Diálogo gerado por dois agentes de chatgpt
  • Categoria de instrução: multi

• Instruções não naturais 2022-12 | Tudo | En | Mc | Papel | Github

  • Editora: Tel Aviv University et al.
  • Tamanho: 240670 instâncias
  • Licença: MIT
  • Fonte: gerado pelo LLMS
  • Categoria de instrução: multi

• WebglM-QA 2023-6 | Tudo | En | Mc | Papel | Github | Conjunto de dados

  • Editor: Tsinghua University et al.
  • Tamanho: 44979 Instâncias
  • Licença: Apache-2.0
  • Fonte: Construa WebGLM-QA via LLM In-Context Bootstrapping
  • Categoria de instrução: Open QA

• Wizard_evol_instruct_196k 2023-6 | Tudo | En | Mc | Papel | Github | Conjunto de dados

  • Editor: Microsoft et al.
  • Tamanho: 196k instâncias
  • Licença: -
  • Fonte: Evoluir Instruções através do Método Evol-Instruct
  • Categoria de instrução: multi

• Wizard_evol_instruct_70k 2023-5 | Tudo | En | Mc | Papel | Github | Conjunto de dados

  • Editor: Microsoft et al.
  • Tamanho: 70 mil instâncias
  • Licença: -
  • Fonte: Evoluir Instruções através do Método Evol-Instruct
  • Categoria de instrução: multi

• WildChat 2024-5 | Parcial | Multi | Mc | Papel | Conjunto de dados

  • Editor: Cornell University et al.
  • Tamanho: 1039785 Instâncias
  • Licença: Licença de Impacto AI2
  • Fonte: conversas entre usuários e chatgpt, GPT-4
  • Categoria de instrução: multi

Coleção e melhoria dos conjuntos de dados existentes (IC)

• CrossFit 2021-4 | Tudo | En | Ci | Papel | Github

  • Editora: Universidade do Sul da Califórnia
  • Tamanho: 269 conjuntos de dados
  • Licença: -
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• DialogStudio 2023-7 | Tudo | En | Ci | Papel | Github | Conjunto de dados

  • Editor: Salesforce AI et al.
  • Tamanho: 87 conjuntos de dados
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• Dynosaur 2023-5 | Tudo | En | Ci | Papel | Github | Conjunto de dados | Site

  • Editora: Ucla et al.
  • Tamanho: 801900 Instâncias
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• Flan-mini 2023-7 | Tudo | En | Ci | Papel | Github | Conjunto de dados

  • Editor: Universidade de Tecnologia e Design de Cingapura
  • Tamanho: 1,34M instâncias
  • Licença: cc
  • Fonte: coleta e melhoria de várias instruções de ajuste fino conjuntos de dados
  • Categoria de instrução: multi

• Flan 2021 2021-9 | Tudo | Multi | Ci | Papel | Github

  • Editor: Google Research
  • Tamanho: 62 conjuntos de dados
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• Flan 2022 2023-1 | Parcial | Multi | Ci | Papel | Github | Conjunto de dados

  • Editor: Google Research
  • Tamanho: 1836 conjuntos de dados
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de várias instruções de ajuste fino conjuntos de dados
  • Categoria de instrução: multi

• InstructDial 2022-5 | Tudo | En | Ci | Papel | Github

  • Editora: Universidade Carnegie Mellon
  • Tamanho: 59 conjuntos de dados
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• Instruções naturais 2021-4 | Tudo | En | Ci | Papel | Github | Conjunto de dados

  • Editora: Allen Institute for Ai et al.
  • Tamanho: 61 conjuntos de dados
  • Licença: Apache-2.0
  • Fonte: coleta e melhoria de vários conjuntos de dados de PNL
  • Categoria de instrução: multi

• OIG 2023-3 | Tudo | En | Ci | Conjunto de dados

  • Editor: Laion
  • Tamanho: 3878622 Instâncias
  • Licença: Apache-2.0
  • Fonte: Coleção e melhoria de vários conjuntos de dados
  • Categoria de instrução: multi

• Platypus aberto 2023-8 | Tudo | En | Ci | Papel | Github | Conjunto de dados | Site

  • Editor: Universidade de Boston
  • Tamanho: 24926 instâncias
  • Licença: -
  • Fonte: Coleção e melhoria de vários conjuntos de dados
  • Categoria de instrução: multi

• Banco Opt-IML 2022-12 | Não | Multi | CI | Paper | Github

  • Publisher: Meta AI
  • Size: 2000 datasets
  • License: MIT
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• PromptSource 2022-2 | All | EN | CI | Paper | Github

  • Publisher: Brown University et al.
  • Size: 176 datasets
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• SUPER-NATURAL INSTRUCTIONS 2022-4 | All | Multi | CI | Paper | Github

  • Publisher: Univ. of Washington et al.
  • Size: 1616 datasets
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• T0 2021-10 | All | EN | CI | Paper | Dataset1 | Dataset2

  • Publisher: Hugging Face et al.
  • Size: 62 datasets
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• UnifiedSKG 2022-3 | All | EN | CI | Paper | Github

  • Publisher: The University of Hong Kong et al.
  • Size: 21 datasets
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• xP3 2022-11 | All | Multi (46) | CI | Paper | Github

  • Publisher: Hugging Face et al.
  • Size: 82 datasets
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi

• IEPile 2024-2 | All | EN & ZH | CI | Paper | Github | Conjunto de dados

  • Publisher: Zhejiang University et al.
  • Size: 33 datasets
  • License: CC-BY-NC-SA-4.0
  • Source: Collection and improvement of various IE datasets
  • Instruction Category: Extraction

• KOLLM-Conversations 2024-3 | All | KO | CI | Conjunto de dados

  • Publisher: davidkim205
  • Size: 1122566 instances
  • License: Apache-2.0
  • Source: Collection and improvement of Korean datasets
  • Instruction Category: Multi

HG & CI

• Firefly 2023-4 | All | ZH | HG & CI | Github | Conjunto de dados

  • Publisher: YeungNLP
  • Size: 1649399 instances
  • License: -
  • Source: Collect Chinese NLP datasets and manually generate data related to Chinese culture
  • Instruction Category: Multi

• LIMA-sft 2023-5 | All | EN | HG & CI | Paper | Conjunto de dados

  • Publisher: Meta AI et al.
  • Size: 1330 instances
  • License: CC-BY-NC-SA
  • Source: Manually select from various types of data
  • Instruction Category: Multi

• COIG-CQIA 2024-3 | All | ZH | HG & CI | Paper | Conjunto de dados

  • Publisher: Shenzhen Institute of Advanced Technology et al.
  • Size: 48375 instances
  • License: -
  • Source: Q&A communities, Wikipedia, examinations, existing NLP datasets
  • Instruction Category: Multi

HG & MC

• InstructGPT-sft 2022-3 | Not | EN | HG & MC | Papel
  • Publisher: OpenAI
  • Size: 14378 instances
  • License: -
  • Source: Platform Q&A data and manual labeling
  • Instruction Category: Multi

CI & MC

• Alpaca_GPT4_data 2023-4 | All | EN | CI & MC | Paper | Github

  • Publisher: Microsoft Research
  • Size: 52K instances
  • License: Apache-2.0
  • Source: Generated by GPT-4 with Aplaca_data prompts
  • Instruction Category: Multi

• Alpaca_GPT4_data_zh 2023-4 | All | ZH | CI & MC | Github | Conjunto de dados

  • Publisher: Microsoft Research
  • Size: 52K instances
  • License: Apache-2.0
  • Source: Generated by GPT-4 with Alpaca_data prompts translated into Chinese by ChatGPT
  • Instruction Category: Multi

• Bactrain-X 2023-5 | All | Multi (52) | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: MBZUAI
  • Size: 3484884 instances
  • License: CC-BY-NC-4.0
  • Source: Generated by GPT-3.5-Turbo with Aplaca_data and databricks-dolly-15K prompts translated into 51 languages by Google Translate API
  • Instruction Category: Multi

• Baize 2023-3 | Partial | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: University of California et al.
  • Size: 210311 instances
  • License: GPL-3.0
  • Source: Sample seeds from specific datasets to create multi-turn dialogues using ChatGPT
  • Instruction Category: Multi

• GPT4All 2023-3 | All | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: nomic-ai
  • Size: 739259 instances
  • License: MIT
  • Source: Generated by GPT-3.5-Turbo with other datasets' prompts
  • Instruction Category: Multi

• GuanacoDataset 2023-3 | All | Multi | CI & MC | Dataset | Site

  • Publisher: JosephusCheung
  • Size: 534530 instances
  • License: GPL-3.0
  • Source: Expand upon the initial 52K dataset from the Alpaca model
  • Instruction Category: Multi

• LaMini-LM 2023-4 | All | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Monash University et al.
  • Size: 2585615 instances
  • License: CC-BY-NC-4.0
  • Source: Generated by ChatGPT with synthetic and existing prompts
  • Instruction Category: Multi

• LogiCoT 2023-5 | All | EN & ZH | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Westlake University et al.
  • Size: 604840 instances
  • License: CC-BY-NC-ND-4.0
  • Source: Expand the datasets using GPT-4
  • Instruction Category: Reasoning

• LongForm 2023-4 | All | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: LMU Munich et al.
  • Size: 27739 instances
  • License: MIT
  • Source: Select documents from existing corpora and generating prompts for the documents using LLMs
  • Instruction Category: Multi

• Luotuo-QA-B 2023-5 | All | EN & ZH | CI & MC | Github | Conjunto de dados

  • Publisher: Luotuo
  • Size: 157320 instances
  • License: Apache-2.0 & CC0
  • Source: Use LLMs to generate Q&A pairs on CSL, arXiv, and CNN-DM datasets
  • Instruction Category: Multi

• OpenOrca 2023-6 | All | Multi | CI & MC | Paper | Conjunto de dados

  • Publisher: Microsoft Researc
  • Size: 4233923 instances
  • License: MIT
  • Source: Expand upon the Flan 2022 dataset using GPT-3.5-Turbo and GPT-4
  • Instruction Category: Multi

• Wizard_evol_instruct_zh 2023-5 | All | ZH | CI & MC | Github | Conjunto de dados

  • Publisher: Central China Normal University et al.
  • Size: 70K instances
  • License: CC-BY-4.0
  • Source: Generated by GPT with Wizard_evol_instruct prompts translated into Chinese
  • Instruction Category: Multi

• Lithuanian-QA-v1 2024-8 | All | LT | CI & MC | Paper | Conjunto de dados

  • Publisher: Neurotechnology
  • Size: 13848 instances
  • License: CC-BY-4.0
  • Source: Use ChatGPT to generate Q&A pairs on Wikipedia corpus
  • Instruction Category: Multi

• LongWriter-6K 2024-8 | All | EN & ZH | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Tsinghua University et al.
  • Size: 6000 instances
  • License: Apache-2.0
  • Source: Generated by GPT-4o with open-source datasets' prompts
  • Instruction Category: Multi

HG & CI & MC

• COIG 2023-4 | All | ZH | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: BAAI
  • Size: 191191 instances
  • License: Apache-2.0
  • Source: Translated instructions, Leetcode, Chinese exams, etc.
  • Instruction Category: Multi

• HC3 2023-1 | All | EN & ZH | HG & CI & MC | Paper | Github | Dataset1 | Dataset2

  • Publisher: SimpleAI
  • Size: 37175 instances
  • License: CC-BY-SA-4.0
  • Source: Human-Q&A pairs and ChatGPT-Q&A pairs from Q&A platforms, encyclopedias, etc.
  • Instruction Category: Multi

• Phoenix-sft-data-v1 2023-5 | All | Multi | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 464510 instances
  • License: CC-BY-4.0
  • Source: Collected multi-lingual instructions, post-translated multi-lingual instructions, self-generated user-centered multi-lingual instructions
  • Instruction Category: Multi

• TigerBot_sft_en 2023-5 | Partial | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: TigerBot
  • Size: 677117 instances
  • License: Apache-2.0
  • Source: Self-instruct, human-labeling, open-source data cleaning
  • Instruction Category: Multi

• TigerBot_sft_zh 2023-5 | Partial | ZH | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: TigerBot
  • Size: 530705 instances
  • License: Apache-2.0
  • Source: Self-instruct, human-labeling, open-source data cleaning
  • Instruction Category: Multi

• Aya Collection 2024-2 | All | Multi (114) | HG & CI & MC | Paper | Dataset | Site

  • Publisher: Cohere For AI Community et al.
  • Size: 513M instances
  • License: Apache-2.0
  • Source: Templated data, Translated data and Aya Dataset
  • Instruction Category: Multi

• REInstruct 2024-8 | Not | EN | HG & CI & MC | Paper | Github

  • Publisher: Chinese Information Processing Laboratory et al.
  • Size: 35K instances
  • License: -
  • Source: Automatically constructing instruction data from the C4 corpus using a small amount of manually annotated seed instruction data
  • Instruction Category: Multi

Domain-specific Instruction Fine-tuning Datasets

The domain-specific instruction fine-tuning datasets are constructed for a particular domain by formulating instructions that encapsulate knowledge and task types closely related to that domain.

Dataset information format：

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Source:
  - Instruction Category:
  - Domain:

Médico

• ChatDoctor 2023-3 | All | EN | HG & MC | Paper | Github | Conjunto de dados

  • Publisher: University of Texas Southwestern Medical Center et al.
  • Size: 115K instances
  • License: Apache-2.0
  • Source: Real conversations between doctors and patients & Generated by ChatGPT
  • Instruction Category: Multi
  • Domain: Medical

• ChatMed_Consult_Dataset 2023-5 | All | ZH | MC | Github | Conjunto de dados

  • Publisher: michael-wzhu
  • Size: 549326 instances
  • License: CC-BY-NC-4.0
  • Source: Generated by GPT-3.5-Turbo
  • Instruction Category: Multi
  • Domain: Medical

• CMtMedQA 2023-8 | All | ZH | HG | Paper | Github | Conjunto de dados

  • Publisher: Zhengzhou University
  • Size: 68023 instances
  • License: MIT
  • Source: Real conversations between doctors and patients
  • Instruction Category: Multi
  • Domain: Medical

• DISC-Med-SFT 2023-8 | All | ZH | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Fudan University et al.
  • Size: 464898 instances
  • License: Apache-2.0
  • Source: Open source datasets & Manually selected data
  • Instruction Category: Multi
  • Domain: Medical

• HuatuoGPT-sft-data-v1 2023-5 | All | ZH | HG & MC | Paper | Github | Conjunto de dados

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 226042 instances
  • License: Apache-2.0
  • Source: Real conversations between doctors and patients & Generated by ChatGPT
  • Instruction Category: Multi
  • Domain: Medical

• Huatuo-26M 2023-5 | Partial | ZH | CI | Paper | Github

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 26504088 instances
  • License: Apache-2.0
  • Source: Collection and improvement of various datasets
  • Instruction Category: Multi
  • Domain: Medical

• MedDialog 2020-4 | All | EN & ZH | HG | Paper | Github

  • Publisher: UC San Diego
  • Size: 3.66M instances
  • License: -
  • Source: Real conversations between doctors and patients
  • Instruction Category: Multi
  • Domain: Medical

• Medical Meadow 2023-4 | All | EN | HG & CI | Paper | Github | Conjunto de dados

  • Publisher: University Hospital Aachen et al.
  • Size: 160076 instances
  • License: GPL-3.0
  • Source: Crawl data from the Internet & Collection and improvement of various NLP datasets
  • Instruction Category: Multi
  • Domain: Medical

• Medical-sft 2023-5 | All | EN & ZH | CI | Github | Conjunto de dados

  • Publisher: Ming Xu
  • Size: 2.07M instances
  • License: Apache-2.0
  • Source: Collection and improvement of various NLP datasets
  • Instruction Category: Multi
  • Domain: Medical

• QiZhenGPT-sft-20k 2023-5 | Partial | ZH | CI | Github | Conjunto de dados

  • Publisher: Zhejiang University
  • Size: 20K instances
  • License: GPL-3.0
  • Source: Collection and improvement of various datasets
  • Instruction Category: Multi
  • Domain: Medical

• ShenNong_TCM_Dataset 2023-6 | All | ZH | MC | Github | Conjunto de dados

  • Publisher: michael-wzhu
  • Size: 112565 instances
  • License: Apache-2.0
  • Source: Generated by ChatGPT
  • Instruction Category: Multi
  • Domain: Medical

Código

• Code_Alpaca_20K 2023-3 | All | EN & PL | MC | Github | Conjunto de dados

  • Publisher: Sahil Chaudhary
  • Size: 20K instances
  • License: Apache-2.0
  • Source: Generated by Text-Davinci-003
  • Instruction Category: Code
  • Domain: Code

• CodeContest 2022-3 | All | EN & PL | CI | Paper | Github

  • Publisher: DeepMind
  • Size: 13610 instances
  • License: Apache-2.0
  • Source: Collection and improvement of various datasets
  • Instruction Category: Code
  • Domain: Code

• CommitPackFT 2023-8 | All | EN & PL (277) | HG | Paper | Github | Conjunto de dados

  • Publisher: Bigcode
  • Size: 702062 instances
  • License: MIT
  • Source: GitHub Action dump
  • Instruction Category: Code
  • Domain: Code

• ToolAlpaca 2023-6 | All | EN & PL | HG & MC | Paper | Github

  • Publisher: Chinese Information Processing Laboratory et al.
  • Size: 3928 instances
  • License: Apache-2.0
  • Source: Manually filter APIs & Generated by ChatGPT
  • Instruction Category: Code
  • Domain: Code

• ToolBench 2023-7 | All | EN & PL | HG & MC | Paper | Github

  • Publisher: Tsinghua University et al.
  • Size: 126486 instances
  • License: Apache-2.0
  • Source: Manually filter APIs & Generated by ChatGPT
  • Instruction Category: Code
  • Domain: Code

Jurídico

• DISC-Law-SFT 2023-9 | Partial | ZH | HG & CI & MC | Paper | Github | Site

  • Publisher: Fudan University et al.
  • Size: 403K instances
  • License: Apache-2.0
  • Source: Open source datasets & Legal-related Text Content & Generated by GPT-3.5-Turbo
  • Instruction Category: Multi
  • Domain: Law

• HanFei 1.0 2023-5 | All | ZH | - | Github | Conjunto de dados

  • Publisher: Chinese Academy of Sciences et al.
  • Size: 255K instances
  • License: Apache-2.0
  • Source: Filter legal-related data according to rules
  • Instruction Category: Multi
  • Domain: Law

• LawGPT_zh 2023-5 | Partial | ZH | CI & MC | Github | Conjunto de dados

  • Publisher: Shanghai Jiao Tong University
  • Size: 200K instances
  • License: -
  • Source: Real conversations & Generated by ChatGPT
  • Instruction Category: Multi
  • Domain: Law

• Lawyer LLaMA_sft 2023-5 | Partial | ZH | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Peking Universit
  • Size: 21476 instances
  • License: Apache-2.0
  • Source: Generated by ChatGPT with other datasets' prompts
  • Instruction Category: Multi
  • Domain: Law

Matemática

• BELLE_School_Math 2023-5 | All | ZH | MC | Github | Conjunto de dados

  • Publisher: BELLE
  • Size: 248481 instances
  • License: GPL-3.0
  • Source: Generated by ChatGPT
  • Instruction Category: Math
  • Domain: Math

• Goat 2023-5 | All | EN | HG | Paper | Github | Conjunto de dados

  • Publisher: National University of Singapore
  • Size: 1746300 instances
  • License: Apache-2.0
  • Source: Artificially synthesized data
  • Instruction Category: Math
  • Domain: Math

• MWP 2021-9 | All | EN & ZH | CI | Paper | Github | Conjunto de dados

  • Publisher: Xihua University et al.
  • Size: 251598 instances
  • License: MIT
  • Source: Collection and improvement of various datasets
  • Instruction Category: Math
  • Domain: Math

• OpenMathInstruct-1 2024-2 | All | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: NVIDIA
  • Size: 1.8M instances
  • License: NVIDIA License
  • Source: GSM8K and MATH datasets (original questions); Generated using Mixtral-8×7B model
  • Instruction Category: Math
  • Domain: Math

Educação

• Child_chat_data 2023-8 | All | ZH | HG & MC | Github

  • Publisher: Harbin Institute of Technology et al.
  • Size: 5000 instances
  • License: -
  • Source: Real conversations & Generated by GPT-3.5-Turbo
  • Instruction Category: Multi
  • Domain: Education

• Educhat-sft-002-data-osm 2023-7 | All | EN & ZH | CI | Paper | Github | Conjunto de dados

  • Publisher: East China Normal University et al.
  • Size: 4279419 instances
  • License: CC-BY-NC-4.0
  • Source: Collection and improvement of various datasets
  • Instruction Category: Multi
  • Domain: Education

• TaoLi_data 2023-X | All | ZH | HG & CI | Github | Conjunto de dados

  • Publisher: Beijing Language and Culture University et al.
  • Size: 88080 instances
  • License: Apache-2.0
  • Source: Collection and improvement of various datasets & Manually extract dictionary data
  • Instruction Category: Multi
  • Domain: Education

Outro

• DISC-Fin-SFT 2023-10 | Partial | ZH | HG & CI & MC | Paper | Github | Site

  • Publisher: Fudan University et al.
  • Size: 246K instances
  • License: Apache-2.0
  • Source: Open source datasets & Manually collect financial data & ChatGPT assistance
  • Instruction Category: Multi
  • Domain: Financial

• AlphaFin 2024-3 | All | EN & ZH | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: South China University of Technology et al.
  • Size: 167362 instances
  • License: Apache-2.0
  • Source: Traditional research datasets, real-time financial data, handwritten CoT data
  • Instruction Category: Multi
  • Domain: Financial

• GeoSignal 2023-6 | Partial | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Shanghai Jiao Tong University et al.
  • Size: 22627272 instances
  • License: Apache-2.0
  • Source: Open source datasets & Geoscience-related Text Content & Generated by GPT-4
  • Instruction Category: Multi
  • Domain: Geoscience

• MeChat 2023-4 | All | ZH | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Zhejiang University et al.
  • Size: 56K instances
  • License: CC0-1.0
  • Source: Based on PsyQA dataset with the proposed SMILE method
  • Instruction Category: Multi
  • Domain: Mental Health

• Mol-Instructions 2023-6 | All | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Zhejiang University et al.
  • Size: 2043586 instances
  • License: CC-BY-4.0
  • Source: Molecule-oriented, Protein-oriented, Biomolecular text instructions
  • Instruction Category: Multi
  • Domain: Biology

• Owl-Instruction 2023-9 | All | EN & ZH | HG & MC | Paper | Github

  • Publisher: Beihang University et al.
  • Size: 17858 instances
  • License: -
  • Source: Generated by GPT-4 & Manual verification
  • Instruction Category: Multi
  • Domain: IT

• PROSOCIALDIALOG 2022-5 | All | EN | HG & MC | Paper | Conjunto de dados

  • Publisher: Allenai
  • Size: 165681 instances
  • License: CC-BY-4.0
  • Source: Generated by humans with GPT-3 created prompts
  • Instruction Category: Social Norms
  • Domain: Social Norms

• TransGPT-sft 2023-7 | All | ZH | HG | Github | Conjunto de dados

  • Publisher: Beijing Jiaotong University
  • Size: 58057 instances
  • License: Apache-2.0
  • Source: Manually collect traffic-related data
  • Instruction Category: Multi
  • Domain: Transportation

Preference Datasets

Preference datasets are collections of instructions that provide preference evaluations for multiple responses to the same instruction input.

Preference Evaluation Methods

The preference evaluation methods for preference datasets can be categorized into voting, sorting, scoring, and other methods. Datasets are classified based on preference evaluation methods.

Dataset information format：

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Domain:
  - Instruction Category: 
  - Preference Evaluation Method: 
  - Source: 

Voto

• Chatbot_arena_conversations 2023-6 | All | Multi | HG & MC | Paper | Conjunto de dados

  • Publisher: UC Berkeley et al.
  • Size: 33000 instances
  • License: CC-BY-4.0 & CC-BY-NC-4.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Generated by twenty LLMs & Manual judgment

• hh-rlhf 2022-4 | All | EN | HG & MC | Paper1 | Paper2 | Github | Conjunto de dados

  • Publisher: Anthropic
  • Size: 169352 instances
  • License: MIT
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Generated by LLMs & Manual judgment

• MT-Bench_human_judgments 2023-6 | All | EN | HG & MC | Paper | Github | Dataset | Site

  • Publisher: UC Berkeley et al.
  • Size: 3.3K instances
  • License: CC-BY-4.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Generated by LLMs & Manual judgment

• PKU-SafeRLHF 2023-7 | Partial | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Peking University
  • Size: 361903 instances
  • License: CC-BY-NC-4.0
  • Domain: Social Norms
  • Instruction Category: Social Norms
  • Preference Evaluation Method: VO-H
  • Source: Generated by LLMs & Manual judgment

• SHP 2021-10 | All | EN | HG | Paper | Github | Conjunto de dados

  • Publisher: Stanford
  • Size: 385563 instances
  • License: -
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Reddit data & Manual judgment

• Zhihu_rlhf_3k 2023-4 | All | ZH | HG | Conjunto de dados

  • Publisher: Liyucheng
  • Size: 3460 instances
  • License: CC-BY-2.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Zhihu data & Manual judgment

• Summarize_from_Feedback 2020-9 | All | EN | HG & CI | Paper | Conjunto de dados

  • Publisher: OpenAI
  • Size: 193841 instances
  • License: -
  • Domain: News
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H & SC-H
  • Source: Open source datasets & Manual judgment and scoring

• CValues 2023-7 | All | ZH | MC | Paper | Github | Conjunto de dados

  • Publisher: Alibaba Group
  • Size: 145K instances
  • License: Apache-2.0
  • Domain: Social Norms
  • Instruction Category: Social Norms
  • Preference Evaluation Method: VO-M
  • Source: Generated by LLMs & Evaluation by the reward model

• huozi_rlhf_data 2024-2 | All | ZH | HG & MC | Github | Conjunto de dados

  • Publisher: Huozi-Team
  • Size: 16918 instances
  • License: Apache-2.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: VO-H
  • Source: Generated by Huozi model & Manual judgment

Organizar

• OASST1_pairwise_rlhf_reward 2023-5 | All | Multi | CI | Conjunto de dados
  • Publisher: Tasksource
  • Size: 18918 instances
  • License: Apache-2.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SO-H
  • Source: OASST1 datasets & Manual sorting

Pontuação

• Stack-Exchange-Preferences 2021-12 | All | EN | HG | Paper | Conjunto de dados

  • Publisher: Anthropic
  • Size: 10807695 instances
  • License: CC-BY-SA-4.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-H
  • Source: Stackexchange data & Manual scoring

• WebGPT 2021-12 | All | EN | HG & CI | Paper | Conjunto de dados

  • Publisher: OpenAI
  • Size: 19578 instances
  • License: -
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-H
  • Source: Open source datasets & Manual scoring

• Alpaca_comparison_data 2023-3 | All | EN | MC | Github

  • Publisher: Stanford Alpaca
  • Size: 51K instances
  • License: Apache-2.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-M
  • Source: Generated by three LLMs & GPT-4 scoring

• Stable_Alignment 2023-5 | All | EN | MC | Paper | Github

  • Publisher: Dartmouth College et al.
  • Size: 169K instances
  • License: Apache-2.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-M
  • Source: Generated by LLMs & Model scoring

• UltraFeedback 2023-10 | All | EN | CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Tsinghua University et al.
  • Size: 63967 instances
  • License: MIT
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-M
  • Source: Generated by seventeen LLMs & Model scoring

• OpenHermesPreferences 2024-2 | All | EN | CI & MC | Conjunto de dados

  • Publisher: Argilla et al.
  • Size: 989490 instances
  • License: -
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SO-M
  • Source: OpenHermes-2.5 dataset & Model sorting

• HelpSteer 2023-11 | All | EN | HG & CI & MC | Paper | Conjunto de dados

  • Publisher: NVIDIA
  • Size: 37120 instances
  • License: CC-BY-4.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-H
  • Source: Generated by LLMs & Manual judgment

• HelpSteer2 2024-6 | All | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: NVIDIA
  • Size: 21362 instances
  • License: CC-BY-4.0
  • Domain: General
  • Instruction Category: Multi
  • Preference Evaluation Method: SC-H
  • Source: Generated by LLMs & Manual judgment

Outro

• Medical-rlhf 2023-5 | All | ZH | CI & MC | Github | Conjunto de dados

  • Publisher: Ming Xu
  • Size: 4K instances
  • License: Apache-2.0
  • Domain: Medical
  • Instruction Category: Multi
  • Preference Evaluation Method: Other
  • Source: Response_chosen comes from the doctor's response & Response_rejected comes from the model's response

• PRM800K 2023-5 | All | EN | HG & CI & MC | Paper | Github

  • Publisher: OpenAI
  • Size: 800K instances
  • License: MIT
  • Domain: Math
  • Instruction Category: Math
  • Preference Evaluation Method: Other
  • Source: Generated by LLMs & Mathematical reasoning steps are determined manually

Evaluation Datasets

Evaluation datasets are a carefully curated and annotated set of data samples used to assess the performance of LLMs across various tasks. Datasets are classified based on evaluation domains.

Dataset information format：

 - Dataset name  Release Time | Public or Not | Language | Construction Method | Paper | Github | Dataset | Website
  - Publisher:
  - Size:
  - License:
  - Question Type: 
  - Evaluation Method: 
  - Focus: 
  - Numbers of Evaluation Categories/Subcategories: 
  - Evaluation Category: 

Em geral

• AlpacaEval 2023-5 | All | EN | CI & MC | Paper | Github | Dataset | Site

  • Publisher: Stanford et al.
  • Size: 805 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The performance on open-ended question answering
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Open-ended question answering

• BayLing-80 2023-6 | All | EN & ZH | HG & CI | Paper | Github | Conjunto de dados

  • Publisher: Chinese Academy of Sciences
  • Size: 320 instances
  • License: GPL-3.0
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: Chinese-English language proficiency and multimodal interaction skills
  • Numbers of Evaluation Categories/Subcategories: 9/-
  • Evaluation Category: Writing, Roleplay, Common-sense, Fermi, Counterfactual, Coding, Math, Generic, Knowledge

• BELLE_eval 2023-4 | All | ZH | HG & MC | Paper | Github

  • Publisher: BELLE
  • Size: 1000 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The performance of Chinese language models in following instructions
  • Numbers of Evaluation Categories/Subcategories: 9/-
  • Evaluation Category: Extract, Closed qa, Rewrite, Summarization, Generation, Classification, Brainstorming, Open qa, Others

• CELLO 2023-9 | All | EN | HG | Paper | Github

  • Publisher: Fudan University et al.
  • Size: 523 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The ability of LLMs to understand complex instructions
  • Numbers of Evaluation Categories/Subcategories: 2/10
  • Evaluation Category: Complex task description, Complex input

• MT-Bench 2023-6 | All | EN | HG | Paper | Github | Site

  • Publisher: UC Berkeley et al.
  • Size: 80 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The performance on open-ended question answering
  • Numbers of Evaluation Categories/Subcategories: 8/-
  • Evaluation Category: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, Humanities

• SuperCLUE 2023-7 | Not | ZH | HG & MC | Paper | Github | Website1 | Website2

  • Publisher: CLUE et al.
  • Size: 3754 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: HE & CE
  • Focus: The performance in a Chinese context
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Open multi-turn open questions, OPT objective questions

• Vicuna Evaluation 2023-3 | All | EN | HG | Github | Dataset | Site

  • Publisher: LMSYS ORG
  • Size: 80 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The performance on open-ended question answering
  • Numbers of Evaluation Categories/Subcategories: 9/-
  • Evaluation Category: Generic, Knowledge, Roleplay, Common-sense, Fermi, Counterfactual, Coding, Math, Writing

• CHC-Bench 2024-4 | All | ZH | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Multimodal Art Projection Research Community et al.
  • Size: 214 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: ME
  • Focus: Hard-case Chinese instructions understanding and following
  • Numbers of Evaluation Categories/Subcategories: 8/-
  • Evaluation Category: Writing, Humanity, Science, Role-playing, Reading Comprehension, Math, Hard Cases, Coding

• CIF-Bench 2024-2 | Partial | ZH | HG & CI | Paper | Github | Site

  • Publisher: University of Manchester et al.
  • Size: 15K instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE & ME
  • Focus: Evaluate the zero-shot generalizability of LLMs to the Chinese language
  • Numbers of Evaluation Categories/Subcategories: 10/150
  • Evaluation Category: Chinese culture, Classification, Code, Commonsense, Creative NLG, Evaluation, Grammar, Linguistic, Motion detection, NER

• WildBench 2024-6 | All | EN | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Allen Institute for AI et al.
  • Size: 1024 instances
  • License: AI2 ImpACT License
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: An automated evaluation framework designed to benchmark LLMs using challenging, real-world user queries.
  • Numbers of Evaluation Categories/Subcategories: 11/-
  • Evaluation Category: Information seeking, Coding & Debugging, Creative writing, Reasoning, Planning, Math, Editing, Data analysis, Role playing, Brainstorming, Advice seeking

• SysBench 2024-8 | All | EN | HG | Paper | Github | Conjunto de dados

  • Publisher: Peking University et al.
  • Size: 500 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: Systematically analyze system message following ability
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Constraint complexity, Instruction misalignment, Multi-turn stability

Exame

• AGIEval 2023-4 | All | EN & ZH | HG & CI | Paper | Github | Conjunto de dados

  • Publisher: Microsoft
  • Size: 8062 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Human-centric standardized exams
  • Numbers of Evaluation Categories/Subcategories: 7/20
  • Evaluation Category: Gaokao, SAT, JEC, LSAT, LogiQA, AQuA-RAT, Math

• GAOKAO-Bench 2023-5 | All | ZH | HG | Paper | Github

  • Publisher: Fudan University et al.
  • Size: 2811 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: HE & CE
  • Focus: Chinese Gaokao examination
  • Numbers of Evaluation Categories/Subcategories: 10/-
  • Evaluation Category: Chinese, Mathematics (2 categories), English, Physics, Chemistry, Biology, Politics, History, Geography

• M3Exam 2023-6 | All | Multi (9) | HG | Paper | Github

  • Publisher: Alibaba Group et al.
  • Size: 12317 instances
  • License: -
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The comprehensive abilities in a multilingual and multilevel context using real human exam questions
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Low, Mid, High

Assunto

• ARB 2023-7 | All | EN | CI | Paper | Github

  • Publisher: DuckAI et al.
  • Size: 1207 instances
  • License: MIT
  • Question Type: Multi
  • Evaluation Method: HE & ME
  • Focus: Advanced reasoning problems in multiple fields
  • Numbers of Evaluation Categories/Subcategories: 5/-
  • Evaluation Category: Mathematics, Physics, Law, MCAT(Reading), MCAT(Science)

• C-CLUE 2021-8 | All | ZH | HG | Github | Site

  • Publisher: Tianjin University
  • Tamanho: -
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Classical Chinese language understanding
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Named entity recognition, Relation extraction

• C-Eval 2023-5 | All | ZH | HG & MC | Paper | Github | Dataset | Site

  • Publisher: Shanghai Jiao Tong University
  • Size: 13948 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The advanced knowledge and reasoning abilities in a Chinese context
  • Numbers of Evaluation Categories/Subcategories: 4/52
  • Evaluation Category: STEM, Social Science, Humanity, Other

• CG-Eval 2023-8 | All | ZH | HG | Paper | Github | Dataset | Site

  • Publisher: LanguageX AI Lab et al.
  • Size: 11000 instances
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The generation capabilities of LLMs across various academic disciplines
  • Numbers of Evaluation Categories/Subcategories: 6/55
  • Evaluation Category: Science and engineering, Humanities and social sciences, Mathematical calculations, Medical practitioner qualification Examination, Judicial Examination, Certfied public accountant examination

• LLMEVAL-3 2023-9 | Not | ZH | HG | Github | Site

  • Publisher: Fudan University et al.
  • Size: 200K instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: Subject-specific knowledge capability
  • Numbers of Evaluation Categories/Subcategories: 13/-
  • Evaluation Category: Philosophy, Economics, Law, Education, Literature, History, Science, Engineering, Agriculture, Medicine, Military science, Management, Fine arts

• MMCU 2023-4 | All | ZH | HG | Paper | Github

  • Publisher: LanguageX AI Lab
  • Size: 11845 instances
  • License: -
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Multidisciplinary abilities
  • Numbers of Evaluation Categories/Subcategories: 4/25
  • Evaluation Category: Medicine, Law, Psychology, Education

• MMLU 2020-9 | All | EN | HG | Paper | Github

  • Publisher: UC Berkeley et al.
  • Size: 15908 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Knowledge in academic and professional domains
  • Numbers of Evaluation Categories/Subcategories: 4/57
  • Evaluation Category: Humanities, Social science, STEM, Other

• M3KE 2023-5 | All | ZH | HG | Paper | Github | Conjunto de dados

  • Publisher: Tianjin University et al.
  • Size: 20477 instances
  • License: Apache-2.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Multidisciplinary abilities
  • Numbers of Evaluation Categories/Subcategories: 4/71
  • Evaluation Category: Arts & Humanities, Social sciences, Natural sciences, Other

• SCIBENCH 2023-7 | All | EN | HG | Paper | Github

  • Publisher: University of California et al.
  • Size: 695 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in university-level science and engineering domains
  • Numbers of Evaluation Categories/Subcategories: 3/10
  • Evaluation Category: Physics, Chemistry, Math

• ScienceQA 2022-9 | All | EN | HG | Paper | Github | Site

  • Publisher: University of California et al.
  • Size: 21208 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Science question-answering ability
  • Numbers of Evaluation Categories/Subcategories: 3/26
  • Evaluation Category: Natural science, Social science, Language science

• TheoremQA 2023-5 | All | EN | HG | Paper | Github | Conjunto de dados

  • Publisher: University of Waterloo et al.
  • Size: 800 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Science subject question-answering ability
  • Numbers of Evaluation Categories/Subcategories: 4/39
  • Evaluation Category: Mathematics, Physics, Finance, CS & EE

• XiezhiBenchmark 2023-6 | All | EN & ZH | HG & MC | Paper | Github

  • Publisher: Fudan University et al.
  • Size: 249587 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Multidisciplinary abilities
  • Numbers of Evaluation Categories/Subcategories: 13/516
  • Evaluation Category: Medicine, Literature, Economics, Agronomy, Science, Jurisprudence, History, Art studies, Philosophy, Pedagogy, Military science, Management, Engineering

• CMMLU 2023-6 | All | ZH | HG | Paper | Github | Conjunto de dados

  • Publisher: MBZUAI
  • Size: 11528 instances
  • License: CC-BY-NC-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The knowledge and reasoning capabilities within the Chinese context
  • Numbers of Evaluation Categories/Subcategories: 5/67
  • Evaluation Category: Social science, STEM, Humanities, China specific, Other

• GPQA 2023-11 | All | EN | HG | Paper | Github | Conjunto de dados

  • Publisher: New York University et al.
  • Size: 448 instances
  • License: CC-BY-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The disciplinary knowledge in the fields of biology, physics, and chemistry
  • Numbers of Evaluation Categories/Subcategories: 3/16
  • Evaluation Category: Biology, Physics, Chemistry

• CMATH 2023-6 | All | ZH | HG | Paper | Github | Conjunto de dados

  • Publisher: Xiaomi AI Lab
  • Size: 1698 instances
  • License: CC-BY-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Elementary school math word problems
  • Numbers of Evaluation Categories/Subcategories: 6/-
  • Evaluation Category: Grades 1 to 6 in elementary school

• FineMath 2024-3 | Not | ZH | HG | Papel

  • Publisher: Tianjin University et al.
  • Size: 1584 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: -
  • Focus: Elementary school math word problems
  • Numbers of Evaluation Categories/Subcategories: 6/17
  • Evaluation Category: Number & Operations, Measurement, Data analysis & Probability, Algebra, Geometry, Others

• WYWEB 2023-7 | All | ZH | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Zhejiang University et al.
  • Size: 467200 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Classical Chinese
  • Numbers of Evaluation Categories/Subcategories: 5/9
  • Evaluation Category: Sequence labeling, Sentence classification, Token similarity, Reading comprehension, Translation

• ACLUE 2023-10 | All | ZH | HG & CI | Paper | Github | Conjunto de dados

  • Publisher: Mohamed bin Zayed University of Artificial Intelligence
  • Size: 4967 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Classical Chinese language understanding
  • Numbers of Evaluation Categories/Subcategories: 5/15
  • Evaluation Category: Lexical, Syntactic, Semantic, Inference, Knowledge

• SciKnowEval 2024-6 | All | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Zhejiang University et al.
  • Size: 50048 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: CE & ME
  • Focus: Evaluate the capabilities of LLMs in handling scientific knowledge
  • Numbers of Evaluation Categories/Subcategories: 2/49
  • Evaluation Category: Biology, Chemistry

• C ³ Bench 2024-5 | All | ZH | HG & CI | Papel

  • Publisher: South China University of Technology
  • Size: 50000 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Classical Chinese
  • Numbers of Evaluation Categories/Subcategories: 5/-
  • Evaluation Category: Classification, Retrieval, NER, Punctuation, Translation

• ArabicMMLU 2024-8 | All | AR | HG | Paper | Github | Conjunto de dados

  • Publisher: MBZUAI et al.
  • Size: 14575 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Multi-task language understanding benchmark for the Arabic language
  • Numbers of Evaluation Categories/Subcategories: 5/40
  • Evaluation Category: STEM, Social science, Humanities, Language, Other

• PersianMMLU 2024-4 | All | FA | HG | Paper | Conjunto de dados

  • Publisher: Raia Center for Artificial Intelligence Research et al.
  • Size: 20192 instances
  • License: CC-ND
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Facilitate the rigorous evaluation of LLMs that support the Persian language
  • Numbers of Evaluation Categories/Subcategories: 5/38
  • Evaluation Category: Social science, Humanities, Natural science, Mathematics, Other

• TMMLU+ 2024-3 | All | ZH | HG & CI | Paper | Conjunto de dados

  • Publisher: iKala AI Lab et al.
  • Size: 22690 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Evaluate the language understanding capabilities in Traditional Chinese
  • Numbers of Evaluation Categories/Subcategories: 4/66
  • Evaluation Category: STEM, Social sciences, Humanities, Other

NLU

• CLUE 2020-12 | All | ZH | CI | Paper | Github

  • Publisher: CLUE team
  • Size: 9 datasets
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Natural language understanding capability
  • Numbers of Evaluation Categories/Subcategories: 3/9
  • Evaluation Category: Single-sentence tasks, Sentence pair tasks, Machine reading comprehension tasks

• CUGE 2021-12 | All | EN & ZH | CI | Paper | Site

  • Publisher: Tsinghua University et al.
  • Size: 33.4M instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Natural language understanding capability
  • Numbers of Evaluation Categories/Subcategories: 7/18
  • Evaluation Category: Language understanding (word-sentence or discourse level), Information acquisition and question answering, Language generation, Conversational interaction, Multilingual, Mathematical reasoning

• GLUE 2018-11 | All | EN | CI | Paper | Github | Site

  • Publisher: New York University et al.
  • Size: 9 datasets
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Natural language understanding capability
  • Numbers of Evaluation Categories/Subcategories: 3/9
  • Evaluation Category: Single-sentence tasks, Similarity and paraphrase tasks, Inference tasks

• SuperGLUE 2019-5 | All | EN | CI | Paper | Site

  • Publisher: New York University et al.
  • Size: 8 datasets
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Natural language understanding capability
  • Numbers of Evaluation Categories/Subcategories: 4/8
  • Evaluation Category: Word sense disambiguation, Natural language inference, Coreference resolution, Question answering

• MCTS 2023-6 | All | ZH | HG | Paper | Github

  • Publisher: Beijing Language and Culture University
  • Size: 723 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Text simplification ability
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Text simplification

• RAFT 2021-9 | All | EN | HG & CI | Paper | Dataset | Site

  • Publisher: Ought et al.
  • Size: 28712 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Text classification ability
  • Numbers of Evaluation Categories/Subcategories: 1/11
  • Evaluation Category: Text classification

• SentEval 2018-5 | All | EN | CI | Paper | Github

  • Publisher: Facebook Artificial Intelligence Research
  • Size: 28 datasets
  • License: BSD
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The quality of universal sentence representations
  • Numbers of Evaluation Categories/Subcategories: 1/21
  • Evaluation Category: Universal sentence representations

• LeSC 2024-5 | All | EN & ZH | HG | Paper | Github | Conjunto de dados

  • Publisher: Tsinghua University et al.
  • Size: 600 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The genuine linguistic-cognitive skills of LLMs
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Polysemy

• KoBEST 2022-10 | All | KO | CI | Paper | Conjunto de dados

  • Publisher: University of Oxford et al.
  • Size: 5 datasets
  • License: CC-BY-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Korean balanced evaluation of significant tasks
  • Numbers of Evaluation Categories/Subcategories: 5/-
  • Evaluation Category: KB-BoolQ, KB-COPA, KB-WiC, KB-HellaSwag, KB-SentiNeg

• SarcasmBench 2024-8 | All | EN | CI | Papel

  • Publisher: Tianjin University et al.
  • Size: 58347 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Evaluate LLMs on sarcasm understanding
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Sarcasm understanding

Raciocínio

• Chain-of-Thought Hub 2023-5 | All | EN | CI | Paper | Github

  • Publisher: University of Edinburgh et al.
  • Tamanho: -
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The multi-step reasoning capabilities
  • Numbers of Evaluation Categories/Subcategories: 6/8
  • Evaluation Category: Math, Science, Symbolic, Knowledge, Coding, Factual

• Choice-75 2023-9 | All | EN | HG & CI & MC | Paper | Github

  • Publisher: University of Pittsburgh et al.
  • Size: 650 instances
  • License: -
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: Predict decisions based on descriptive scenarios
  • Numbers of Evaluation Categories/Subcategories: 4/-
  • Evaluation Category: Easy, Medium, Hard, N/A

• NeuLR 2023-6 | All | EN | CI | Paper | Github | Dataset

  • Publisher: Xi'an Jiaotong University et al.
  • Size: 3000 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Logical reasoning capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Deductive, Inductive, Abductive

• TabMWP 2022-9 | All | EN | HG | Paper | Github | Site

  • Publisher: University of California et al.
  • Size: 38431 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Mathematical reasoning ability involving both textual and tabular information
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Mathematical reasoning and table QA

• LILA 2022-10 | All | EN | CI | Paper | Github | Conjunto de dados

  • Publisher: Arizona State Univeristy et al.
  • Size: 317262 instances
  • License: CC-BY-4.0
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Mathematical reasoning across diverse tasks
  • Numbers of Evaluation Categories/Subcategories: 4/23
  • Evaluation Category: Math ability, Language, Knowledge, Format

• MiniF2F_v1 2021-9 | All | EN | HG & CI | Paper | Github

  • Publisher: Ecole Polytechnique et al.
  • Size: 488 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance on formal Olympiad-level mathematics problem statements
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Math

• GameBench 2024-6 | All | EN | HG | Paper | Github | Dataset

  • Publisher: Olin College of Engineering et al.
  • Size: 9 Games
  • License: CC-BY
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Evaluate strategic reasoning abilities of LLM agents
  • Numbers of Evaluation Categories/Subcategories: 6/9
  • Evaluation Category: Abstract Strategy, Non-Deterministic, Hidden Information, Language Communication, Social Deduction, Cooperation

• TableBench 2024-8 | All | EN | HG & CI & MC | Paper | Github | Dataset | Site

  • Publisher: Beihang University et al.
  • Size: 886 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Table question answering (TableQA) capabilities
  • Numbers of Evaluation Categories/Subcategories: 4/18
  • Evaluation Category: Fact checking, Numerical reasoning, Data analysis, Visualization

Conhecimento

• ALCUNA 2023-10 | All | EN | HG | Paper | Github | Dataset

  • Publisher: Peking University
  • Size: 84351 instances
  • License: MIT
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Assess the ability of LLMs to respond to new knowledge
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Knowledge understanding, Knowledge differentiation, Knowledge association

• KoLA 2023-6 | Partial | EN | HG & CI | Paper | Github | Site

  • Publisher: Tsinghua University
  • Size: 2138 instances
  • License: GPL-3.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The ability to grasp and utilize world knowledge
  • Numbers of Evaluation Categories/Subcategories: 4/19
  • Evaluation Category: Knowledge memorization, Knowledge understanding, Knowledge applying, Knowledge creating

• LLMEVAL-2 2023-7 | All | ZH | HG | Github

  • Publisher: Fudan University et al.
  • Size: 480 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: HE & ME
  • Focus: Knowledge capability
  • Numbers of Evaluation Categories/Subcategories: 12/-
  • Evaluation Category: Computer science, Economics, Foreign languages, Law, Mathematics, Medicine, Optics, Physics, Social sciences, Chinese language and literature, Chemistry, Life sciences

• SocKET 2023-5 | All | EN | CI | Paper | Github

  • Publisher: University of Michigan et al.
  • Size: 2616342 instances
  • License: CC-BY-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Mastery of social knowledge
  • Numbers of Evaluation Categories/Subcategories: 4/58
  • Evaluation Category: Classification, Regression, Pair-wise comparison, Span identification

• LMExamQA 2023-6 | All | EN | MC | Paper | Site

  • Publisher: Tsinghua University et al.
  • Size: 10090 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The performance on open-ended question answering
  • Numbers of Evaluation Categories/Subcategories: 3/25
  • Evaluation Category: Knowledge memorization, Knowledge comprehension, Knowledge analysis

• DebateQA 2024-8 | All | EN | HG & CI & MC | Paper | Github | Dataset

  • Publisher: Tsinghua Universty et al.
  • Size: 2941 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: Evaluate the comprehensiveness of perspectives and assess whether the LLM acknowledges the question's debatable nature
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Perspective diversity, Dispute awareness

Long Text

• L-Eval 2023-7 | All | EN | HG & CI | Paper | Github | Dataset

  • Publisher: Fudan University et al.
  • Size: 2043 instances
  • License: GPL-3.0
  • Question Type: SQ
  • Evaluation Method: HE & CE & ME
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 1/18
  • Evaluation Category: Long text task

• LongBench 2023-8 | All | EN & ZH | CI | Paper | Github | Dataset

  • Publisher: Tsinghua University et al.
  • Size: 4750 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 6/21
  • Evaluation Category: Single-doc QA, Multi-doc QA, Summarization, Few-shot learning, Synthetic tasks, Code completion

• LongEval 2023-6 | All | EN | HG | Github | Site

  • Publisher: LMSYS
  • Tamanho: -
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Coarse-grained topic retrieval, Fine-grained line retrieval

• InfiniteBench 2023-11 | All | EN & ZH | HG & CI & MC | Github | Dataset

  • Publisher: Tsinghua University et al.
  • Size: 3932 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: -
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 5/12
  • Evaluation Category: Mathematics, Code, Dialogue, Books, Retrieval

• ZeroSCROLLS 2023-5 | All | EN | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Tel Aviv University et al.
  • Size: 4378 instances
  • License: MIT
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 3/10
  • Evaluation Category: Summarization, Question Answering, Aggregation

• LooGLE 2023-11 | All | EN | HG & CI & MC | Paper | Github | Dataset

  • Publisher: BIGAI et al.
  • Size: 6448 instances
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: HE & CE & ME
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 2/4
  • Evaluation Category: Long dependency tasks, Short dependency tasks

• NAH (Needle-in-a-Haystack) 2023-11 | All | EN | - | Github

  • Publisher: gkamradt et al.
  • Tamanho: -
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Long text task

• CLongEval 2024-3 | All | ZH | HG & CI & MC | Paper | Github | Dataset

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 7267 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 7/-
  • Evaluation Category: Long story QA, Long conversation memory, Long story summarization, Stacked news labeling, Stacked typo detection, Key-passage retrieval, Table querying

• Counting-Stars 2024-3 | All | ZH | HG | Paper | Github | Dataset

  • Publisher: Tencent MLPD
  • Tamanho: -
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Long text task capability
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Long text task

• NeedleBench 2024-7 | All | EN & ZH | HG & CI | Paper | Github

  • Publisher: Shanghai AI Laboratory et al.
  • Tamanho: -
  • License: -
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Assess bilingual long-context capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Single-retrieval, Multi-retrieval, Multi-reasoning

Ferramenta

• API-Bank 2023-4 | All | EN & PL | HG & MC | Paper | Github

  • Publisher: Alibaba DAMO Academy et al.
  • Size: 264 dialogues
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: HE & CE
  • Focus: Plan step-by-step API calls, retrieve relevant APIs, and correctly execute API calls to meet human needs
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Call, Retrieval+Call, Plan+Retrieval+Call

• APIBench 2023-5 | All | EN & PL | HG & MC | Paper | Github | Dataset | Site

  • Publisher: UC Berkeley et al.
  • Size: 16450 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The reasoning ability for calling APIs
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: API call

• ToolBench 2023-5 | All | EN | HG & CI | Paper | Github

  • Publisher: SambaNova Systems et al.
  • Size: 795 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The enhancement in tool manipulation for real-world software tasks
  • Numbers of Evaluation Categories/Subcategories: 8/-
  • Evaluation Category: Open weather, The cat API, Home search, Trip booking, Google sheets, Virtual home, Web shop, Tabletop

• ToolEyes 2024-1 | All | EN | HG | Paper | Github | Conjuntos de dados

  • Publisher: Fudan University
  • Size: 382 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE & ME
  • Focus: The LLMs' tool learning capabilities in authentic scenarios
  • Numbers of Evaluation Categories/Subcategories: 7/41
  • Evaluation Category: Text generation, Data understanding, Real-time search, Application manipulation, Personal life, Information retrieval, Financial transactions

Agente

• AgentBench 2023-8 | All | EN | HG & CI & MC | Paper | Github | Site

  • Publisher: Tsinghua University et al.
  • Size: 1360 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: LLM-as-Agent's reasoning and decision-making abilities
  • Numbers of Evaluation Categories/Subcategories: 8/-
  • Evaluation Category: Operating system, Database, Knowledge graph, Digital card game, Lateral thinking puzzles, House-holding, Web shopping, Web browsing

• SuperCLUE-Agent 2023-10 | Not | ZH | - | Github | Website1 | Website2

  • Publisher: CLUEbenchmark
  • Tamanho: -
  • License: -
  • Question Type: SQ
  • Evaluation Method: -
  • Focus: Agent capabilities of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/10
  • Evaluation Category: Tool utilization, Task planning, Long-term and short-term memory

Código

• BIRD 2023-5 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | Site

  • Publisher: The University of Hong Kong et al.
  • Size: 12751 instances
  • License: CC-BY-NC-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Text-to-SQL parsing
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Text-SQL

• CodeXGLUE 2021-2 | All | EN & PL | CI | Paper | Github | Dataset

  • Publisher: Peking University et al.
  • Size: 4.12M instances
  • License: C-UDA
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Program understanding and generation tasks
  • Numbers of Evaluation Categories/Subcategories: 4/10
  • Evaluation Category: Code-Code, Text-Code, Code-Text, Text-to-Text

• DS-1000 2022-11 | All | EN & PL | HG | Paper | Github | Dataset | Site

  • Publisher: The University of Hong Kong et al.
  • Size: 1000 instances
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Code generation
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Code generation

• HumanEval 2021-7 | All | EN & PL | HG | Paper | Github

  • Publisher: OpenAI et al.
  • Size: 164 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The correctness of problem-solving abilities in the context of program synthesis
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Code generation

• HumanEvalPack 2023-8 | All | EN & PL | HG & CI | Paper | Github | Dataset

  • Publisher: Bigcode
  • Size: 984 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The correctness of problem-solving abilities in the context of program synthesis
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: HumanEvalFix, HumanEvalExplain, HumanEvalSynthesize

• MTPB 2022-3 | All | EN & PL | HG | Paper | Github | Conjunto de dados

  • Publisher: Salesforce Research
  • Size: 115 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Multi-turn Programming
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Code generation

• ODEX 2022-12 | All | Multi & PL | HG & CI | Paper | Github

  • Publisher: Carnegie Mellon University et al.
  • Size: 945 instances
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Natural language to Python code generation
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Code generation

• APPS 2021-5 | All | EN & PL | HG | Paper | Github | Dataset

  • Publisher: UC Berkeley et al.
  • Size: 10000 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The ability to take an arbitrary natural language specification and generate satisfactory Python code
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Code generation

• DomainEval 2024-8 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | Site

  • Publisher: Chinese Academy of Sciences et al.
  • Size: 5892 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Evaluate LLMs' coding capabilities thoroughly
  • Numbers of Evaluation Categories/Subcategories: 6/-
  • Evaluation Category: Computation, Network, Basic operation, System, Visualization, Cryptography

OOD

• BOSS 2023-6 | All | EN | CI | Paper | Github

  • Publisher: Tsinghua University et al.
  • Tamanho: -
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Assess model performance under distribution shifts
  • Numbers of Evaluation Categories/Subcategories: 5/20
  • Evaluation Category: Sentiment analysis, Toxicity detection, Natural language inference, Named entity recognition, Extractive Question answering

• GLUE-X 2022-11 | All | EN | CI | Paper | Github | Dataset

  • Publisher: Westlake University et al.
  • Size: 6404940 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The out-of-distribution (OOD) robustness
  • Numbers of Evaluation Categories/Subcategories: 7/16
  • Evaluation Category: Sentiment analysis, Linguistic acceptability, Textual similarity, Natural language inference, Question answering NLI, Textual entailment, Paraphrase

Lei

• LAiW 2023-10 | Partial | ZH | CI | Paper | Github

  • Publisher: Sichuan University et al.
  • Tamanho: -
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Legal capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/13
  • Evaluation Category: Basic legal NLP, Basic legal application, Complex legal application

• LawBench 2023-9 | All | ZH | HG & CI | Paper | Github | Dataset

  • Publisher: Nanjing University et al.
  • Tamanho: -
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Legal capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/20
  • Evaluation Category: Legal knowledge memorization, Legal knowledge understanding, Legal knowledge applying

• LegalBench 2023-8 | All | EN | HG & CI | Paper | Github | Dataset | Site

  • Publisher: Stanford University et al.
  • Size: 90417 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: HE & CE
  • Focus: Legal reasoning
  • Numbers of Evaluation Categories/Subcategories: 6/162
  • Evaluation Category: Issue-spotting, Rule-recall, Rule-application, Rule-conclusion, Interpretation, Rhetorical-understanding

• LexGLUE 2021-10 | All | EN | CI | Paper | Github

  • Publisher: University of Copenhagen et al.
  • Size: 237014 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Legal capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Multi-label classification, Multi-class classification, Multiple choice QA

• LEXTREME 2023-1 | All | Multi (24) | CI | Paper | Github

  • Publisher: University of Bern et al.
  • Size: 3508603 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Legal capabilities
  • Numbers of Evaluation Categories/Subcategories: 18/-
  • Evaluation Category: Brazilian court decisions, German argument mining, Greek legal code, Swiss judgment prediction, etc.

• SCALE 2023-6 | All | Multi (5) | HG & CI | Paper | Dataset

  • Publisher: University of Bern et al.
  • Size: 1.86M instances
  • License: CC-BY-SA
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Legal multidimensional abilities
  • Numbers of Evaluation Categories/Subcategories: 4/-
  • Evaluation Category: Processing long documents, Utilizing domain specific knowledge, Multilingual understanding, Multitasking

• ArabLegalEval 2024-8 | All | AR | HG & CI & MC | Paper | Github | Dataset

  • Publisher: THIQAH et al.
  • Size: 37853 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: ME
  • Focus: Assess the Arabic legal knowledge of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/-

Médico

• CBLUE 2022-5 | All | ZH | HG & CI | Paper | Github

  • Publisher: Zhejiang University et al.
  • Size: 195820 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Chinese biomedical language understanding
  • Numbers of Evaluation Categories/Subcategories: 5/8
  • Evaluation Category: Information extraction from the medical text, normalization of the medical term, medical text classification, medical sentence similarity estimation, medical QA

• CMB 2023-8 | All | ZH | HG | Paper | Github | Dataset | Site

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 281047 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: HE & CE & ME
  • Focus: The performance of LLMs in the field of medicine
  • Numbers of Evaluation Categories/Subcategories: 2/7
  • Evaluation Category: CMB-Exam, CMB-Clin

• HuaTuo26M-test 2023-5 | All | ZH | CI | Paper | Github | Dataset

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 6000 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Understand and generate complex medical language
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Medical consultant records, Encyclopedias, Knowledge bases

• MultiMedQA 2022-12 | All | EN | HG & CI | Paper | Dataset

  • Publisher: Google Research et al.
  • Size: 212822 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: HE & CE
  • Focus: The performance in medical and clinical applications
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Medical question answering

• PromptCBLUE 2023-4 | All | ZH | CI | Github

  • Publisher: East China Normal University et al.
  • Size: 20640 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in Chinese medical scenarios
  • Numbers of Evaluation Categories/Subcategories: 16/-
  • Evaluation Category: Medical named entity recognition, Medical entity relation extraction, Medical event extraction, etc.

• QiZhenGPT_eval 2023-5 | All | ZH | HG | Github | Dataset

  • Publisher: Zhejiang University et al.
  • Size: 94 instances
  • License: GPL-3.0
  • Question Type: SQ
  • Evaluation Method: HE
  • Focus: Indications for use of drugs
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Drug indication question answering

• CLUE 2024-4 | Partical | EN | HG & CI & MC | Paper | Github

  • Publisher: University Hospital Essen et al.
  • Tamanho: -
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Real-world clinical tasks
  • Numbers of Evaluation Categories/Subcategories: 6/-
  • Evaluation Category: MeDiSumQA, MeDiSumCode, MedNLI, MeQSum, Problem Summary, LongHealth

Financeiro

• BBF-CFLEB 2023-2 | All | ZH | HG & CI | Paper | Github | Site

  • Publisher: Fudan University et al.
  • Size: 11327 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Language understanding and generation tasks in Chinese financial natural language processing
  • Numbers of Evaluation Categories/Subcategories: 6/-
  • Evaluation Category: FinNL, FinNA, FinRE, FinFE, FinQA, FinNSP

• FinancelQ 2023-9 | All | ZH | HG & MC | Github

  • Publisher: Du Xiaoman
  • Size: 7173 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The knowledge and reasoning abilities in financial contexts
  • Numbers of Evaluation Categories/Subcategories: 10/36
  • Evaluation Category: Bank, Fund, Securities, Futures and derivatives, CICE, Actuarial science, Financial planning, CPA, Taxation, Economics

• FinEval 2023-8 | All | ZH | HG | Paper | Github | Dataset | Site

  • Publisher: Shanghai University of Finance and Economics
  • Size: 4661 instances
  • License: CC-BY-NC-SA-4.0
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The performance in the financial domain knowledge
  • Numbers of Evaluation Categories/Subcategories: 4/34
  • Evaluation Category: Finance, Economy, Accounting, Certificate

• FLUE 2022-10 | All | EN | CI | Paper | Site

  • Publisher: Georgia Institute of Technology et al.
  • Size: 26292 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: NLP tasks in the financial domain
  • Numbers of Evaluation Categories/Subcategories: 5/6
  • Evaluation Category: Financial sentiment analysis, News headline classification, Named entity recognition, Structure boundary detection, Question answering

• FinBen 2024-2 | All | EN | CI | Paper | Github

  • Publisher: The Fin AI et al.
  • Size: 69805 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: NLP tasks in the financial domain
  • Numbers of Evaluation Categories/Subcategories: 3/6
  • Evaluation Category: Foundamental tasks, Advanced cognitive engagement, General intelligence

Social Norms

• CrowS-Pairs 2020-11 | All | EN | HG & CI | Paper | Github

  • Publisher: New York University
  • Size: 1508 instances
  • License: CC-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The presence of cultural biases and stereotypes in pretrained language models
  • Numbers of Evaluation Categories/Subcategories: 9/-
  • Evaluation Category: Race, Gender, Sexual orientation, Religion, Age, Nationality, Disability, Physical appearance, Occupation

• SafetyBench 2023-9 | All | EN & ZH | HG & CI & MC | Paper | Github | Dataset | Site

  • Publisher: Tsinghua University et al.
  • Size: 11435 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The safety of LLMs
  • Numbers of Evaluation Categories/Subcategories: 7/-
  • Evaluation Category: Offensiveness, Unfairness and bias, Physical health, Mental Health, Illegal activities, Ethics and morality, Privacy and Property

• Safety-Prompts 2023-4 | Partial | ZH | MC | Paper | Github | Dataset | Site

  • Publisher: Tsinghua University et al.
  • Size: 100K instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: HE & ME
  • Focus: The safety of LLMs
  • Numbers of Evaluation Categories/Subcategories: 2/13
  • Evaluation Category: Typical security scenarios, Instruction attack

• SuperCLUE-Safety 2023-9 | Not | ZH | - | Github | Site

  • Publisher: CLUEbenchmark
  • Size: 4912 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The safety of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/20+
  • Evaluation Category: Traditional security category, Responsible artificial intelligence, Instruction attacks

• TRUSTGPT 2023-6 | All | EN | CI | Paper | Github

  • Publisher: Sichuan University et al.
  • Size: 2000 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in toxicity, bias, and value alignment
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Toxicity, Bias, Value-alignment

Factuality

• FACTOR 2023-7 | Partial | EN | HG & CI & MC | Paper | Github

  • Publisher: AI21 Labs
  • Size: 4030 instances
  • License: MIT
  • Question Type: OQ
  • Evaluation Method: CE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Wiki, News

• FActScore 2023-5 | All | EN | HG & MC | Paper | Github

  • Publisher: University of Washington et al.
  • Size: 500 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: HE & ME
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 7/-
  • Evaluation Category: Single-sentence contradiction (words or beyond words), Page-level contradiction, Subjective, Fact is irrelevant, Wiki is inconsistent & wrong, Annotation error

• FactualityPrompt 2022-6 | All | EN | CI | Paper | Github

  • Publisher: Hong Kong University of Science and Technology et al.
  • Size: 16000 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Factual prompts, Nonfactual prompts

• FreshQA 2023-10 | All | EN | HG | Paper | Github

  • Publisher: Google et al.
  • Size: 600 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: HE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 4/-
  • Evaluation Category: Never-changing, Slow-changing, Fast-changing, False-premise

• HalluQA 2023-10 | All | ZH | HG & MC | Paper | Github

  • Publisher: Fudan University et al.
  • Size: 450 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: ME
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Misleading, Misleading-hard, Knowledge

• HaluEval 2023-5 | All | EN | HG & CI & MC | Paper | Github | Conjunto de dados

  • Publisher: Renmin University of China et al.
  • Size: 35000 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: QA, Dialogue, Summarization

• TruthfulQA 2022-5 | All | EN | HG | Paper | Github

  • Publisher: University of Oxford et al.
  • Size: 817 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE & ME
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 38/-
  • Evaluation Category: Health, Law, Conspiracies, Fiction, Misconceptions, Paranormal, Economics, Biology, Language, Indexical etc.

• UHGEval 2023-11 | All | ZH | HG & MC | Paper | Github | Conjunto de dados

  • Publisher: Renmin University of China et al.
  • Size: 5141 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 3/4
  • Evaluation Category: Discriminative, Selective, Generative

• HaluEval-Wild 2024-3 | Not | EN | HG & CI & MC | Papel

  • Publisher: Carnegie Mellon University
  • Size: 500 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: -
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 5/-
  • Evaluation Category: Out-of-scope information, Complex reasoning, Inappropriate content, Beyond-modality interaction, Confused / Erroneous queries

• RealTime QA 2022-7 | All | EN | HG | Paper | Github | Dataset | Site

  • Publisher: Toyota Technological Institute et al.
  • Tamanho: -
  • License: -
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Latest knowledge Q&A

• ChineseFactEval 2023-9 | All | ZH | HG & MC | Github | Dataset | Site

  • Publisher: Shanghai Jiao Tong University et al.
  • Size: 125 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: -
  • Focus: The factuality of LLMs
  • Numbers of Evaluation Categories/Subcategories: 7/-
  • Evaluation Category: General domain, Scientific research, Medical, Law, Finance, Math, Chinese modern history

• HalluDial 2024-6 | All | EN | CI & MC | Paper | Github | Dataset

  • Publisher: BAAI et al.
  • Size: 146856 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: HE & CE & ME
  • Focus: Automatic dialogue-level hallucination evaluation
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: Hallucination detection, Hallucination localization and explanation

Avaliação

• FairEval 2023-5 | All | EN | CI | Paper | Github | Dataset

  • Publisher: Peking University et al.
  • Size: 80 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in determining the quality of output content from different models
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Evaluate the quality of answers

• LLMEval2 2023-8 | All | Multi | CI | Paper | Github | Dataset

  • Publisher: Chinese Academy of Sciences et al.
  • Size: 2533 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in determining the quality of output content from different models
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Evaluate the quality of answers

• PandaLM_testset 2023-4 | All | EN | HG & MC | Paper | Github

  • Publisher: Peking University et al.
  • Size: 999 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance in determining the quality of output content from different models
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Evaluate the quality of answers

Multitarefa

• BBH 2022-10 | All | EN | CI | Paper | Github

  • Publisher: Google Research et al.
  • Size: 6511 instances
  • License: MIT
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: Challenging tasks that have proven difficult for prior language model evaluations
  • Numbers of Evaluation Categories/Subcategories: 23/27
  • Evaluation Category: Boolean expressions, Causal judgement, Date understanding, Disambiguation QA, etc.

• BIG-Bench 2022-6 | All | Multi | HG & CI | Paper | Github

  • Publisher: Google et al.
  • Tamanho: -
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: CE
  • Focus: The capabilities and limitations of language models
  • Numbers of Evaluation Categories/Subcategories: 95/204
  • Evaluation Category: Linguistics, Child development, Mathematics, Common sense reasoning, Biology, etc.

• CLEVA 2023-8 | All | ZH | HG & CI | Paper | Github | Site

  • Publisher: The Chinese University of Hong Kong et al.
  • Size: 370K instances
  • License: CC-BY-NC-ND-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance of LLMs across various dimensions
  • Numbers of Evaluation Categories/Subcategories: 2/31
  • Evaluation Category: Ability, Application

• CLiB 2023-6 | All | ZH | - | Github

  • Publisher: jeinlee1991
  • Size: 90 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: HE
  • Focus: Multidimensional capabilities
  • Numbers of Evaluation Categories/Subcategories: 4/-
  • Evaluation Category: Classification, Information extraction, Reading comprehension, Tabular question answering

• decaNLP 2018-6 | All | EN | CI | Paper | Github

  • Publisher: Salesforce Research
  • Size: 2010693 instances
  • License: BSD-3-Clause
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Multitask natural language processing capabilities
  • Numbers of Evaluation Categories/Subcategories: 10/-
  • Evaluation Category: Question answering, Machine translaion, Summarization, Natural language inference, Sentiment analysis, Semantic role labeling, Zero-shot relation extraction, Goal-oriented dialogue, Semantic parsing, Pronoun resolution

• FlagEval 2023-6 | Partial | EN & ZH | HG & CI | Github | Site

  • Publisher: BAAI et al.
  • Size: 84433 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: HE & CE
  • Focus: Multi-domain, multi-dimensional capabilities
  • Numbers of Evaluation Categories/Subcategories: 3/21
  • Evaluation Category: Choice qa, Classification, Generation qa

• HELM 2022-11 | All | EN | CI | Paper | Github | Site

  • Publisher: Stanford University et al.
  • Tamanho: -
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: HE & CE
  • Focus: Evaluate LLMs on a wide range of scenarios and metrics
  • Numbers of Evaluation Categories/Subcategories: 73/-
  • Evaluation Category: Question answering, Information retrieval, Sentiment analysis, Toxicity detection, Aspirational scenarios, etc.

• LLMEVAL-1 2023-5 | All | ZH | HG | Github

  • Publisher: Fudan University et al.
  • Size: 453 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: HE & ME
  • Focus: Multidimensional capabilities
  • Numbers of Evaluation Categories/Subcategories: 17/-
  • Evaluation Category: Fact-based question answering, Reading comprehension, Framework generation, Paragraph rewriting, etc.

• LMentry 2023-7 | All | EN | HG | Paper | Github

  • Publisher: Tel Aviv University et al.
  • Size: 110703 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The performance on challenging tasks
  • Numbers of Evaluation Categories/Subcategories: 25/-
  • Evaluation Category: Sentence containing word, Sentence not containing word, Word containing letter, Word not containing letter, etc.

• AlignBench 2023-11 | All | ZH | HG & MC | Paper | Github | Dataset

  • Publisher: Tsinghua University et al.
  • Size: 683 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: ME
  • Focus: Evaluate the alignment of LLMs on Chinese multitasks.
  • Numbers of Evaluation Categories/Subcategories: 8/-
  • Evaluation Category: Fundamental language ability, Advanced Chinese understanding, Open-ended questions, Writing ability, Logical reasoning, Mathematics, Task-oriented role play,
  • Professional knowledge

Multilíngue

• XNLI 2018-10 | All | Multi (15) | HG | Paper | Github

  • Publisher: Facebook AI et al.
  • Size: 112500 instances
  • License: CC-BY-NC-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Multilingual NLI
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Multilingual natural language inference

• XTREME 2020-3 | All | Multi (40) | CI | Paper | Github | Site

  • Publisher: Carnegie Mellon University et al.
  • Tamanho: -
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The cross-lingual generalization capabilities
  • Numbers of Evaluation Categories/Subcategories: 4/9
  • Evaluation Category: Classification, Structured prediction, QA, Retrieval

• MGSM 2022-10 | All | Multi (10) | CI | Paper | Github | Dataset

  • Publisher: Google Research et al.
  • Size: 2580 instances
  • License: CC-BY-SA-4.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Multilingual mathematical reasoning abilities
  • Numbers of Evaluation Categories/Subcategories: 1/-
  • Evaluation Category: Math

Outro

• EcomGPT_eval 2023-8 | All | EN & ZH | CI | Paper | Github

  • Publisher: Alibaba
  • Size: 6000 instances
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: E-commerce-related tasks
  • Numbers of Evaluation Categories/Subcategories: 4/12
  • Evaluation Category: Classification, Generation, Extraction, Others
  • Domain: E-commerce

• FewCLUE 2021-7 | Partial | ZH | CI | Paper | Github | Site

  • Publisher: CLUE team
  • Size: 9 datasets
  • License: -
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Compare different few-shot learning methods
  • Numbers of Evaluation Categories/Subcategories: 3/9
  • Evaluation Category: Single sentence tasks, Sentence pair tasks, Reading comprehension
  • Domain: Few-shot learning

• GeoBench 2023-6 | All | EN | HG | Paper | Github

  • Publisher: Shanghai Jiao Tong University et al.
  • Size: 2517 instances
  • License: Apache-2.0
  • Question Type: Multi
  • Evaluation Method: HE & CE & ME
  • Focus: LLMs' performance in understanding and utilizing geoscience knowledge
  • Numbers of Evaluation Categories/Subcategories: 2/-
  • Evaluation Category: NPEE, APTest
  • Domain: Geoscience

• Owl-Bench 2023-9 | All | EN & ZH | HG | Paper | Github

  • Publisher: Beihang University et al.
  • Size: 1317 instances
  • License: -
  • Question Type: Multi
  • Evaluation Method: ME
  • Focus: The performance in IT-related tasks
  • Numbers of Evaluation Categories/Subcategories: 9/-
  • Evaluation Category: Information security, Application, System architecture, Software architecture, Middleware, Network, Operating system, Infrastructure, Database
  • Domain: IT

• MINT 2023-9 | All | EN | CI | Paper | Github | Dataset | Site

  • Publisher: University of Illinois Urbana-Champaign et al.
  • Size: 586 instances
  • License: Apache-2.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: Solve complex tasks through multi-turn interactions using tools and leveraging natural language feedback
  • Numbers of Evaluation Categories/Subcategories: 3/-
  • Evaluation Category: Code generation, Decision making, Reasoning
  • Domain: Multi-turn interactions

• PromptBench 2023-6 | All | EN | CI | Paper | Github

  • Publisher: Microsoft Research et al.
  • Size: 583884 instances
  • License: MIT
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The models' robustness
  • Numbers of Evaluation Categories/Subcategories: 10/15
  • Evaluation Category: Sentiment analysis, Grammar correctness, Duplicate sentence detection, Natural language inference, etc.
  • Domain: Robustness

• EmotionBench 2023-8 | All | EN | HG & MC | Paper | Github

  • Publisher: The Chinese University of Hong Kong et al.
  • Tamanho: -
  • License: GPL-3.0
  • Question Type: SQ
  • Evaluation Method: CE
  • Focus: The empathy ability
  • Numbers of Evaluation Categories/Subcategories: 8/36
  • Evaluation Category: Anger, Anxiety, Depression, Frustration, Jealous, Guilt, Fear, Embarrassment
  • Domain: Sentiment

Evaluation Platform

• CLUE Benchmark Series

  • SuperCLUE-Agent
  • SuperCLUE-Auto
  • SuperCLUE-Math6
  • SuperCLUE-Safety
  • SuperCLUE-Code3
  • SuperCLUE-Video
  • SuperCLUE-RAG
  • SuperCLUE-Industry
  • SuperCLUE-Role

• OpenLLM Leaderboard

• OpenCompass

• MTEB Leaderboard

• C-MTEB Leaderboard

Traditional NLP Datasets

Diverging from instruction fine-tuning datasets, we categorize text datasets dedicated to natural language tasks before the widespread adoption of LLMs as traditional NLP datasets.

Dataset information format：

 - Dataset name  Release Time | Language | Paper | Github | Dataset | Website
  - Publisher:
  - Train/Dev/Test/All Size: 
  - License:
  - Number of Entity Categories: (NER Task)
  - Number of Relationship Categories: (RE Task)

Question Answering

The task of question-answering requires the model to utilize its knowledge and reasoning capabilities to respond to queries based on provided text (which may be optional) and questions.

Reading Comprehension

The task of reading comprehension entails presenting a model with a designated text passage and associated questions, prompting the model to understand the text for the purpose of answering the questions.

Selection & Judgment

• BoolQ 2019-5 | EN | Paper | Github

  • Publisher: University of Washington et al.
  • Train/Dev/Test/All Size: 9427/3270/3245/15942
  • License: CC-SA-3.0

• CosmosQA 2019-9 | EN | Paper | Github | Dataset | Site

  • Publisher: University of Illinois Urbana-Champaign et al.
  • Train/Dev/Test/All Size: 25588/3000/7000/35588
  • License: CC-BY-4.0

• CondaQA 2022-11 | EN | Paper | Github | Dataset

  • Publisher: Carnegie Mellon University et al.
  • Train/Dev/Test/All Size: 5832/1110/7240/14182
  • License: Apache-2.0

• PubMedQA 2019-9 | EN | Paper | Github | Dataset | Site

  • Publisher: University of Pittsburgh et al.
  • Train/Dev/Test/All Size: -/-/-/273.5K
  • License: MIT

• MultiRC 2018-6 | EN | Paper | Github | Conjunto de dados

  • Publisher: University of Pennsylvania et al.
  • Train/Dev/Test/All Size: -/-/-/9872
  • License: MultiRC License

• RACE 2017-4 | EN | Paper | Dataset | Site

  • Publisher: Carnegie Mellon University
  • Train/Dev/Test/All Size: 87866/4887/4934/97687
  • License: -

• C3 2019-4 | ZH | Paper | Github | Site

  • Publisher: Cornell University et al.
  • Train/Dev/Test/All Size: 11869/3816/3892/19577
  • License: -

• ReClor 2020-2 | EN | Paper | Site

  • Publisher: National University of Singapore
  • Train/Dev/Test/All Size: 4638/500/1000/6138
  • License: -

• DREAM 2020-2 | EN | Paper | Github | Site

  • Publisher: National University of Singapore
  • Train/Dev/Test/All Size: 4638/500/1000/6138
  • License: -

• QuAIL 2020-4 | EN | Paper | Site

  • Publisher: University of Massachusetts Lowell
  • Train/Dev/Test/All Size: 10346/-/2164/12510
  • License: CC-NC-SA-4.0

• DuReader Yes/No 2019-12 | ZH | Github1 | Github2

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: 75K/5.5K/11K/91.5K
  • License: Apache-2.0

• MCTest 2013-10 | EN | Paper | Dataset

  • Publisher: Microsoft Research
  • Train/Dev/Test/All Size: 1200/200/600/2000
  • License: -

Cloze Test

• ChID 2019-6 | ZH | Paper | Github | Dataset

  • Publisher: Tsinghua University et al.
  • Train/Dev/Test/All Size: 605k/23.2K/83.3K/711.5K
  • License: Apache-2.0

• LAMBADA 2016-6 | EN | Paper | Dataset | Site

  • Publisher: University of Trento et al.
  • Train/Dev/Test/All Size: 2662/4869/5153/12684
  • License: CC-BY-4.0

• CLOTH 2018-10 | EN | Paper | Dataset

  • Publisher: Carnegie Melon University
  • Train/Dev/Test/All Size: 76850/11067/11516/99433
  • License: MIT

• CMRC2019 2020-12 | ZH | Paper | Github | Site

  • Publisher: Harbin Institute of Technology et al.
  • Train/Dev/Test/All Size: 100009/3053/5118/108180
  • License: CC-BY-SA-4.0

Answer Extraction

• SQuAD 2016-11 | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 87599/10570/9533/107702
  • License: CC-BY-4.0

• SQuAD 2.0 2018-6 | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 130319/11873/8862/151054
  • License: CC-BY-SA-4.0

• HOTPOTQA 2018-9 | EN | Paper | Dataset | Site

  • Publisher: Carnegie Mellon University et al.
  • Train/Dev/Test/All Size: 90447/7405/7405/105257
  • License: CC-BY-SA-4.0

• TriviaQA 2017-7 | EN | Paper | Github | Dataset

  • Publisher: Univ. of Washington et al.
  • Train/Dev/Test/All Size: -/-/-/95000
  • License: Apache-2.0

• Natural Questions 2019-X | EN | Paper | Github | Dataset

  • Publisher: Google Research
  • Train/Dev/Test/All Size: 307372/7830/7842/323044
  • License: CC-BY-4.0

• ReCoRD 2018-10 | EN | Paper | Site

  • Publisher: Johns Hopkins University et al.
  • Train/Dev/Test/All Size: 100730/10000/10000/120730
  • License: -

• QuAC 2018-8 | EN | Paper | Dataset | Site

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 83568/7354/7353/98407
  • License: CC-BY-SA-4.0

• TyDiQA 2020-3 | Multi (11) | Paper | Github | Dataset

  • Publisher: Google Research
  • Train/Dev/Test/All Size: 116916/18670/18751/154337
  • License: Apache-2.0

• CMRC2018 2019-11 | ZH | Paper | Github

  • Publisher: Harbin Institute of Technology et al.
  • Train/Dev/Test/All Size: 10321/3351/4895/18567
  • License: CC-BY-SA-4.0

• Adversarial QA 2020-2 | EN | Paper | Github | Dataset

  • Publisher: University College London
  • Train/Dev/Test/All Size: 30000/3000/3000/36000
  • License: MIT

• Quoref 2019-8 | EN | Paper | Site

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 19399/2418/2537/24354
  • License: CC-BY-4.0

• MLQA 2020-7 | Multi (7) | Paper | Github | Conjunto de dados

  • Publisher: Facebook AI Research et al.
  • Train/Dev/Test/All Size: -/4199/42246/46445
  • License: CC-BY-SA-3.0

• DuReader Robust 2020-3 | ZH | Paper | Github1 | Github2

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: 15K/1.4K/4.8K/21.2K
  • License: Apache-2.0

• DuReader Checklist 2021-3 | ZH | Github1 | Github2

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: 3K/1.1K/4.5K/8.6K
  • License: Apache-2.0

• CUAD 2021-3 | EN | Paper | Dataset

  • Publisher: UC Berkeley et al.
  • Train/Dev/Test/All Size: 22450/-/4182/26632
  • License: CC-BY-4.0

• MS MARCO 2016-11 | EN | Paper | Github | Dataset

  • Publisher: Microsoft AI & Research
  • Train/Dev/Test/All Size: 808731/101093/101092/1010916
  • License: MIT

Unrestricted QA

• DROP 2019-6 | EN | Paper | Site

  • Publisher: University of California et al.
  • Train/Dev/Test/All Size: 77409/9536/9622/96567
  • License: CC-BY-4.0

• CoQA 2018-8 | EN | Paper | Site

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: -/-/-/127K
  • License: -

• QASPER 2021-5 | EN | Paper | Site

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: -/-/-/5049
  • License: CC-BY-4.0

• DuoRC 2018-7 | EN | Paper | Dataset | Site

  • Publisher: IBM Research et al.
  • Train/Dev/Test/All Size: 130261/27914/27914/186089
  • License: MIT

• DuReader 2.0 2018-4 | ZH | Paper | Github1 | Github2

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: -/-/-/200K
  • License: Apache-2.0

Knowledge QA

In the knowledge QA task, models respond to questions by leveraging world knowledge, common sense, scientific insights, domain-specific information, and more.

• ARC 2018-3 | EN | Paper | Site

  • Publisher: AI2
  • Train/Dev/Test/All Size: 3370/869/3548/7787
  • License: CC-BY-SA

• CommonsenseQA 2018-11 | EN | Paper | Github | Dataset | Site

  • Publisher: Tel-Aviv University et al.
  • Train/Dev/Test/All Size: 9797/1225/1225/12247
  • License: MIT

• OpenBookQA 2018-10 | EN | Paper | Github | Dataset

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 4957/500/500/5957
  • License: Apache-2.0

• PIQA 2019-11 | EN | Paper | Github | Dataset

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 16.1K/1.84K/3.08K/21.02K
  • License: MIT

• JEC-QA 2019-11 | EN | Paper | Github | Dataset | Site

  • Publisher: Tsinghua University et al.
  • Train/Dev/Test/All Size: -/-/26365/26365
  • License: CC-NC-ND-4.0

• CMD 2019-X | ZH | Github | Dataset

  • Publisher: Toyhom
  • Train/Dev/Test/All Size: -/-/-/792099
  • License: MIT

• cMedQA2 2018-11 | ZH | Paper | Dataset

  • Publisher: National University of Defense Technology
  • Train/Dev/Test/All Size: 100000/4000/4000/108000
  • License: GPL-3.0

• HEAD-QA 2019-7 | EN & ES | Paper | Github | Dataset | Site

  • Publisher: Universidade da Coruna
  • Train/Dev/Test/All Size: 2657/1366/2742/13530
  • License: MIT

• SciQ 2017-9 | EN | Paper | Dataset | Site

  • Publisher: University College London et al.
  • Train/Dev/Test/All Size: 11679/1000/1000/13679
  • License: CC-BY-NC-3.0

• WikiQA 2015-9 | EN | Paper | Dataset | Site

  • Publisher: Georgia Institute of Technology et al.
  • Train/Dev/Test/All Size: 2118/296/633/3047
  • License: Microsoft Research Data License

• ECQA 2021-8 | EN | Paper | Github

  • Publisher: IIT Delhi et al.
  • Train/Dev/Test/All Size: 7598/1090/2194/10882
  • License: CDLA-Sharing-1.0

• PsyQA 2021-6 | ZH | Paper | Github

  • Publisher: The CoAI group et al.
  • Train/Dev/Test/All Size: -/-/-/22346
  • License: PsyQA User Agreement

• WebMedQA 2018-12 | ZH | Paper | Github

  • Publisher: Chinese Academy of Sciences et al.
  • Train/Dev/Test/All Size: 50610/6337/6337/63284
  • License: Apache-2.0

• WebQuestions 2013-10 | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 3778/-/2032/5810
  • License: -

Reasoning QA

The focal point of reasoning QA tasks is the requirement for models to apply abilities such as logical reasoning, multi-step inference, and causal reasoning in answering questions.

• STRATEGYQA 2021-1 | EN | Paper | Site

  • Publisher: Tel Aviv University et al.
  • Train/Dev/Test/All Size: 2290/-/490/2780
  • License: MIT

• COPA 2011-6 | EN | Paper | Site

  • Publisher: Indiana University et al.
  • Train/Dev/Test/All Size: -/500/500/1000
  • License: BSD 2-Clause

• HellaSwag 2019-7 | EN | Paper | Github

  • Publisher: University of Washington et al.
  • Train/Dev/Test/All Size: 39905/10042/10003/59950
  • License: MIT

• StoryCloze 2016-6 | EN | Paper | Conjunto de dados

  • Publisher: University of Rochester et al.
  • Train/Dev/Test/All Size: -/1871/1871/3742
  • License: -

• Social IQa 2019-4 | EN | Paper | Dataset

  • Publisher: AI2
  • Train/Dev/Test/All Size: 33410/1954/-/35364
  • License: -

• LogiQA 2020-7 | EN & ZH | Paper | Github

  • Publisher: Fudan University et al.
  • Train/Dev/Test/All Size: 7376/651/651/8678
  • License: -

• PROST 2021-8 | EN | Paper | Github | Dataset

  • Publisher: University of Colorado Boulder
  • Train/Dev/Test/All Size: -/-/18736/18736
  • License: Apache-2.0

• QuaRTz 2019-11 | EN | Paper | Dataset | Site

  • Publisher: AI2
  • Train/Dev/Test/All Size: 2696/384/784/3864
  • License: CC-BY-4.0

• WIQA 2019-9 | EN | Paper | Dataset | Site

  • Publisher: AI2
  • Train/Dev/Test/All Size: 29808/6894/3993/40695
  • License: -

• QASC 2019-10 | EN | Paper | Dataset | Site

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 8134/926/920/9980
  • License: CC-BY-4.0

• QuaRel 2018-11 | EN | Paper | Site

  • Publisher: AI2
  • Train/Dev/Test/All Size: 1941/278/552/2771
  • License: CC-BY-4.0

• ROPES 2019-8 | EN | Paper | Dataset | Site

  • Publisher: AI2
  • Train/Dev/Test/All Size: 10K/1.6K/1.7K/13.3K
  • License: CC-BY-4.0

• CREAK 2021-9 | EN | Paper | Github

  • Publisher: The University of Texas at Austin
  • Train/Dev/Test/All Size: 10176/1371/1371/13418
  • License: MIT

Recognizing Textual Entailment

The primary objective of tasks related to Recognizing Textual Entailment (RTE) is to assess whether information in one textual segment can be logically inferred from another.

• ANLI 2019-10 | EN | Paper | Github | Dataset

  • Publisher: UNC Chapel Hill et al.
  • Train/Dev/Test/All Size: 162865/3200/3200/169265
  • License: CC-NC-4.0

• RTE - | EN | Paper1 | Paper2 | Paper3 | Paper4 | Dataset

  • Publisher: The PASCAL Recognising Textual Entailment Challenge
  • Train/Dev/Test/All Size: 2.49K/277/3K/5.77K
  • License: CC-BY-4.0

• WANLI 2022-1 | EN | Paper | Dataset

  • Publisher: University of Washington et al.
  • Train/Dev/Test/All Size: 102885/-/5000/107885
  • License: CC-BY-4.0

• MedNLI 2018-8 | EN | Paper | Github | Dataset | Site

  • Publisher: University of Massachusetts Lowell et al.
  • Train/Dev/Test/All Size: 11232/1395/1422/14049
  • License: -

• CommitmentBank 2019-X | EN | Paper | Github | Dataset

  • Publisher: The Ohio State University et al.
  • Train/Dev/Test/All Size: -/-/-/1200
  • License: -

• MultiNLI 2018-6 | EN | Paper | Conjunto de dados

  • Publisher: New York University
  • Train/Dev/Test/All Size: 392702/19647/-/412349
  • License: -

• SNLI 2015-8 | EN | Paper | Dataset

  • Publisher: Stanford Linguistics et al.
  • Train/Dev/Test/All Size: 550152/10000/10000/570152
  • License: CC-BY-SA-4.0

• OCNLI 2020-10 | ZH | Paper | Github

  • Publisher: Indiana University et al.
  • Train/Dev/Test/All Size: 50K/3K/3K/56K
  • License: CC-BY-NC-2.0

• CMNLI 2020-12 | ZH | Github | Dataset

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 391783/12426/13880/418089
  • License: -

• CINLID 2021-4 | ZH | Dataset

  • Publisher: Gao et al.
  • Train/Dev/Test/All Size: 80124/-/26708/106832
  • License: -

Matemática

Mathematical assignments commonly involve standard mathematical calculations, theorem validations, and mathematical reasoning tasks, among others.

• GSM8K 2021-10 | EN | Paper | Github | Dataset

  • Publisher: OpenAI
  • Train/Dev/Test/All Size: 7.5K/-/1K/8.5K
  • License: MIT

• SVAMP 2021-3 | EN | Paper | Github

  • Publisher: Microsoft Research India
  • Train/Dev/Test/All Size: -/-/-/1000
  • License: MIT

• ASDiv 2021-6 | EN | Paper | Github | Dataset

  • Publisher: Institute of Information Science
  • Train/Dev/Test/All Size: -/-/-/2305
  • License: CC-BY-NC-4.0

• MATH 2021-3 | EN | Paper | Github | Dataset

  • Publisher: UC Berkeley et al.
  • Train/Dev/Test/All Size: 7500/-/5000/12500
  • License: MIT

• Ape210K 2020-9 | ZH | Paper | Github

  • Publisher: Yuanfudao AI Lab et al.
  • Train/Dev/Test/All Size: 200488/5000/5000/210488
  • License: -

• Math23K 2017-9 | ZH | Paper | Github

  • Publisher: Tencent AI Lab
  • Train/Dev/Test/All Size: -/-/-/23161
  • License: MIT

• MathQA 2019-5 | EN | Paper | Dataset | Site

  • Publisher: University of Washington et al.
  • Train/Dev/Test/All Size: 29837/4475/2985/37297
  • License: Apache-2.0

• AQUA-RAT 2017-7 | EN | Paper | Github | Dataset

  • Publisher: DeepMind
  • Train/Dev/Test/All Size: 100949/250/250/101499
  • License: Apache-2.0

• NaturalProofs 2021-4 | EN | Paper | Github

  • Publisher: University of Washington et al.
  • Train/Dev/Test/All Size: -/-/-/80795
  • License: MIT

Coreference Resolution

The core objective of tasks related to coreference resolution is the identification of referential relationships within texts.

• WSC 2012-X | EN | Paper | Dataset

  • Publisher: University of Toronto et al.
  • Train/Dev/Test/All Size: -/-/285/285
  • License: CC-BY-4.0

• DPR 2012-7 | EN | Paper | Dataset

  • Publisher: University of Texas at Dallas
  • Train/Dev/Test/All Size: 1322/-/564/1886
  • License: -

• WinoGrande 2019-7 | EN | Paper | Github | Dataset

  • Publisher: AI2 et al.
  • Train/Dev/Test/All Size: 63238/1267/1767/66272
  • License: CC-BY

• WiC 2018-8 | EN | Paper | Site

  • Publisher: University of Cambridge
  • Train/Dev/Test/All Size: 5428/638/1400/7466
  • License: CC-NC-4.0

• WinoWhy 2020-7 | EN | Paper | Github

  • Publisher: HKUST
  • Train/Dev/Test/All Size: -/-/-/43972
  • License: MIT

• CLUEWSC2020 2020-12 | ZH | Paper | Github1 | Github2

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 1244/304/290/1838
  • License: -

Sentiment Analysis

The sentiment analysis task, commonly known as emotion classification, seeks to analyze and deduce the emotional inclination of provided texts, commonly categorized as positive, negative, or neutral sentiments.

• IMDB 2011-6 | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 25000/-/25000/50000
  • License: -

• Sentiment140 2009-X | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 1600000/-/359/1600359
  • License: -

• SST-2 2013-10 | EN | Paper | Dataset

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 67349/872/1821/70042
  • License: -

• EPRSTMT 2021-7 | ZH | Paper | Github

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 32/32/1363/20992
  • License: -

Semantic Matching

The task of semantic matching entails evaluating the semantic similarity or degree of correspondence between two sequences of text.

• MRPC 2005-X | EN | Papel

  • Publisher: Microsoft Research
  • Train/Dev/Test/All Size: 4076/-/1725/5801
  • License: -

• QQP 2018-11 | EN | Paper | Dataset

  • Publisher: New York University et al.
  • Train/Dev/Test/All Size: 364K/-/-/364K
  • License: -

• PAWS 2019-6 | EN | Paper | Github | Dataset

  • Publisher: Google AI Language
  • Train/Dev/Test/All Size: 49401/8000/8000/65401
  • License: -

• STSB 2017-8 | Multi (10) | Paper | Github | Dataset | Site

  • Publisher: Google Research et al.
  • Train/Dev/Test/All Size: 5749/1500/1379/8628
  • License: -

• AFQMC 2020-12 | ZH | Papel

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 34.3K/4.3K/3.9K/42.5K
  • License: -

• BQ 2018-10 | ZH | Paper | Dataset

  • Publisher: Harbin Institute of Technology et al.
  • Train/Dev/Test/All Size: 100000/10000/10000/120000
  • License: -

• LCQMC 2018-8 | ZH | Papel

  • Publisher: Harbin Institute of Technology et al.
  • Train/Dev/Test/All Size: 238766/8802/12500/260068
  • License: CC-BY-4.0

• PAWS-X 2019-8 | Multi (6) | Paper | Github

  • Publisher: Google Research
  • Train/Dev/Test/All Size: 296406/11815/11844/320065
  • License: -

• BUSTM 2021-7 | ZH | Paper | Github

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 32/32/3772/8087
  • License: -

• DuQM 2021-9 | ZH | Paper | Github1 | Github2

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: -/-/-/10121
  • License: Apache-2.0

Text Generation

The narrow definition of text generation tasks is bound by provided content and specific requirements. It involves utilizing benchmark data, such as descriptive terms and triplets, to generate corresponding textual descriptions.

• CommonGen 2019-11 | EN | Paper | Github | Dataset

  • Publisher: University of Southern California et al.
  • Train/Dev/Test/All Size: 67389/4018/1497/72904
  • License: MIT

• DART 2020-7 | EN | Paper | Github | Dataset

  • Publisher: Yale University et al.
  • Train/Dev/Test/All Size: 30526/2768/6959/40253
  • License: MIT

• E2E 2017-6 | EN | Paper | Github | Dataset

  • Publisher: Heriot-Watt University
  • Train/Dev/Test/All Size: 42061/4672/4693/51426
  • License: CC-BY-SA-3.0

• WebNLG 2017-7 | EN & RU | Paper | Github | Dataset

  • Publisher: LORIA et al.
  • Train/Dev/Test/All Size: 49665/6490/7930/64085
  • License: CC-BY-NC-SA-4.0

Text Translation

Text translation involves transforming text from one language to another.

• WMT - | Multi | Site

  • Publisher: ACL et al.
  • Train/Dev/Test/All Size: -/-/-/-
  • License: -

• NLLB 2022-7 | Multi | Paper | Github

  • Publisher: NLLB Team et al.
  • Train/Dev/Test/All Size: -/-/-/-
  • License: MIT

• IWSLT 2017 2017-12 | Multi (11) | Paper | Dataset | Site

  • Publisher: FBK et al.
  • Train/Dev/Test/All Size: 1108475/4442/41921/1154838
  • License: CC-BY-NC-ND-4.0

Text Summarization

The task of text summarization pertains to the extraction or generation of a brief summary or headline from an extended text to encapsulate its primary content.

• AESLC 2019-7 | EN | Paper | Github | Dataset

  • Publisher: Yale University et al.
  • Train/Dev/Test/All Size: 14436/1960/1906/18302
  • License: CC-BY-NC-SA-4.0

• CNN-DM 2017-4 | EN | Paper | Dataset

  • Publisher: Stanford University et al.
  • Train/Dev/Test/All Size: 287113/13368/11490/311971
  • License: Apache-2.0

• MultiNews 2019-7 | EN | Paper | Github | Dataset

  • Publisher: Yale University
  • Train/Dev/Test/All Size: 44972/5622/5622/56216
  • License: -

• Newsroom 2018-6 | EN | Paper | Dataset

  • Publisher: Cornell University
  • Train/Dev/Test/All Size: 995041/108837/108862/1212740
  • License: -

• SAMSum 2019-11 | EN | Paper | Conjunto de dados

  • Publisher: Cornell University
  • Train/Dev/Test/All Size: 14732/818/819/16369
  • License: CC-BY-NC-ND-4.0

• XSum 2018-10 | EN | Paper | Github | Dataset

  • Publisher: University of Edinburgh
  • Train/Dev/Test/All Size: 204045/11332/11334/226711
  • License: MIT

• Opinion Abstracts 2016-6 | EN | Paper | Dataset

  • Publisher: Northeastern University et al.
  • Train/Dev/Test/All Size: 5990/-/-/5990
  • License: -

• WikiLingua 2020-10 | Multi (18) | Paper | Github | Dataset

  • Publisher: Columbia University et al.
  • Train/Dev/Test/All Size: -/-/-/770087
  • License: CC-BY-3.0

• LCSTS 2015-6 | ZH | Paper | Dataset

  • Publisher: Harbin Institute of Technology
  • Train/Dev/Test/All Size: 2400000/10000/1000/2411000
  • License: CC-BY-4.0

• CNewSum 2021-10 | ZH | Paper | Github | Dataset | Site

  • Publisher: ByteDance
  • Train/Dev/Test/All Size: 275596/14356/14355/304307
  • License: Apache-2.0

• XL-Sum 2021-8 | Multi (45) | Paper | Dataset

  • Publisher: BUET et al.
  • Train/Dev/Test/All Size: 1122857/114198/114198/1351253
  • License: CC-BY-NC-SA-4.0

• WikiHow 2018-10 | EN | Paper | Github

  • Publisher: University of California
  • Train/Dev/Test/All Size: -/-/-/230K
  • License: CC-BY-NC-SA

• MediaSum 2021-3 | EN | Paper | Github | Conjunto de dados

  • Publisher: Microsoft Cognitive Services Research Group
  • Train/Dev/Test/All Size: 443596/10000/10000/463596
  • License: -

Text Classification

Text classification tasks aim to assign various text instances to predefined categories, comprising text data and category labels as pivotal components.

• AGNEWS 2015-9 | EN | Paper | Dataset | Site

  • Publisher: New York University
  • Train/Dev/Test/All Size: 120000/-/7600/127600
  • License: -

• TNEWS 2020-11 | ZH | Paper | Github | Dataset

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 53.3K/10K/10K/73.3K
  • License: -

• IFLYTEK 2020-12 | ZH | Papel

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 12.1K/2.6K/2.6K/17.3K
  • License: -

• MARC 2020-11 | Multi (6) | Paper | Dataset

  • Publisher: Amazon et al.
  • Train/Dev/Test/All Size: 1200000/30000/30000/1260000
  • License: -

• THUCNews 2016-X | ZH | Github | Site

  • Publisher: Tsinghua University
  • Train/Dev/Test/All Size: -/-/-/1672165
  • License: MIT

• CSLDCP 2021-7 | ZH | Paper | Github | Site

  • Publisher: CLUE team
  • Train/Dev/Test/All Size: 536/536/4783/23966
  • License: -

Text Quality Evaluation

The task of text quality evaluation, also referred to as text correction, involves the identification and correction of grammatical, spelling, or language usage errors in text.

• CoLA 2018-5 | EN | Paper | Site

  • Publisher: New York University
  • Train/Dev/Test/All Size: 8511/1043/-/9554
  • License: CC-BY-4.0

• SIGHAN - | ZH | Paper1 | Paper2 | Paper3 | Dataset1 | Dataset2 | Dataset3

  • Publisher: Chaoyang Univ. of Technology et al.
  • Train/Dev/Test/All Size: 6476/-/3162/9638
  • License: -

• YACLC 2021-12 | ZH | Paper | Github

  • Publisher: Beijing Language and Culture University et al.
  • Train/Dev/Test/All Size: 8000/1000/1000/10000
  • License: -

• CSCD-IME 2022-11 | ZH | Paper | Github

  • Publisher: Tencent Inc
  • Train/Dev/Test/All Size: 30000/5000/5000/40000
  • License: MIT

Text-to-Code

The Text-to-Code task involves models converting user-provided natural language descriptions into computer-executable code, thereby achieving the desired functionality or operation.

• MBPP 2021-8 | EN & PL | Paper | Github

  • Publisher: Google Research
  • Train/Dev/Test/All Size: -/-/974/974
  • License: -

• DuSQL 2020-11 | ZH & PL | Paper | Dataset

  • Publisher: Baidu Inc. et al.
  • Train/Dev/Test/All Size: 18602/2039/3156/23797
  • License: -

• CSpider 2019-11 | ZH & PL | Paper | Github | Site

  • Publisher: Westlake University
  • Train/Dev/Test/All Size: -/-/-/10181
  • License: CC-BY-SA-4.0

• Spider 2018-9 | EN & PL | Paper | Github | Site

  • Publisher: Yale University
  • Train/Dev/Test/All Size: -/-/-/10181
  • License: CC-BY-SA-4.0

Named Entity Recognition

The Named Entity Recognition (NER) task aims to discern and categorize named entities within a given text.

• WUNT2017 2017-9 | EN | Paper | Dataset

  • Publisher: Johns Hopkins University et al.
  • Train/Dev/Test/All Size: 3394/1009/1287/5690
  • License: CC-BY-4.0
  • Number of Entity Categories: 6

• Few-NERD 2021-5 | EN | Paper | Github | Dataset | Site

  • Publisher: Tsinghua University et al.
  • Train/Dev/Test/All Size: -/-/-/188200
  • License: CC-BY-SA-4.0
  • Number of Entity Categories: 66

• CoNLL2003 2003-6 | EN & DE | Paper | Dataset

  • Publisher: University of Antwerp
  • Train/Dev/Test/All Size: 14041/3250/3453/20744
  • License: -
  • Number of Entity Categories: 4

• OntoNotes 5.0 2013-10 | Multi (3) | Paper | Dataset | Site

  • Publisher: Boston Childrens Hospital and Harvard Medical School et al.
  • Train/Dev/Test/All Size: 59924/8528/8262/76714
  • License: -
  • Number of Entity Categories: 18

• MSRA 2006-7 | ZH | Paper | Dataset

  • Publisher: University of Chicago
  • Train/Dev/Test/All Size: 46364/-/4365/50729
  • License: CC-BY-4.0
  • Number of Entity Categories: 3

• Youku NER 2019-6 | ZH | Paper | Github | Conjunto de dados

  • Publisher: Singapore University of Technology and Design et al.
  • Train/Dev/Test/All Size: 8001/1000/1001/10002
  • License: -
  • Number of Entity Categories: 9

• Taobao NER 2019-6 | ZH | Paper | Github | Dataset

  • Publisher: Singapore University of Technology and Design et al.
  • Train/Dev/Test/All Size: 6000/998/1000/7998
  • License: -
  • Number of Entity Categories: 9

• Weibo NER 2015-9 | ZH | Paper | Github | Dataset

  • Publisher: Johns Hopkins University
  • Train/Dev/Test/All Size: 1350/269/270/1889
  • License: CC-BY-SA-3.0
  • Number of Entity Categories: 4

• CLUENER 2020-1 | ZH | Paper | Github | Dataset

  • Publisher: CLUE Organization
  • Train/Dev/Test/All Size: 10748/1343/1345/13436
  • License: -
  • Number of Entity Categories: 10

• Resume 2018-7 | ZH | Paper | Github

  • Publisher: Singapore University of Technology and Design
  • Train/Dev/Test/All Size: 3821/463/477/4761
  • License: -
  • Number of Entity Categories: 8

Relation Extraction

The endeavor of Relation Extraction (RE) necessitates the identification of connections between entities within textual content. This process typically includes recognizing and labeling pertinent entities, followed by the determination of the specific types of relationships that exist among them.

• Dialogue RE 2020-7 | EN & ZH | Paper | Github | Site

  • Publisher: Tencent AI Lab et al.
  • Train/Dev/Test/All Size: 6100/2034/2034/10168
  • License: -
  • Number of Relationship Categories: 36

• TACRED 2017-9 | EN | Paper | Dataset | Site

  • Publisher: Stanford University
  • Train/Dev/Test/All Size: 68124/22631/15509/106264
  • License: LDC
  • Number of Relationship Categories: 42

• DocRED 2019-7 | EN | Paper | Github

  • Publisher: Tsinghua University et al.
  • Train/Dev/Test/All Size: 1546589/12332/12842/1571763
  • License: MIT
  • Number of Relationship Categories: 96

• FewRel 2018-10 | EN | Paper1 | Paper2 | Github | Site

  • Publisher: Tsinghua University
  • Train/Dev/Test/All Size: -/-/-/70000
  • License: CC-BY-SA-4.0
  • Number of Relationship Categories: 100

Multitarefa

Multitask datasets hold significance as they can be concurrently utilized for different categories of NLP tasks.

• CSL 2022-9 | ZH | Paper | Github

  • Publisher: School of Information Engineering et al.
  • Train/Dev/Test/All Size: -/-/-/396209
  • License: Apache-2.0

• QED 2021-3 | EN | Paper | Github

  • Publisher: Stanford University et al.
  • Train/Dev/Test/All Size: 7638/1355/-/8993
  • License: CC-BY-SA-3.0 & GFDL

• METS-CoV 2022-9 | EN | Paper | Github

  • Publisher: Zhejiang University et al.
  • Train/Dev/Test/All Size: -/-/-/-
  • License: Apache-2.0

Multi-modal Large Language Models (MLLMs) Datasets

Pre-training Corpora

Documents

• mOSCAR : A large-scale multilingual and multimodal document-level corpus

  • Paper: mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
  • Github: https://oscar-project.github.io/documentation/versions/mOSCAR/

• OBELISC : An open web-scale filtered dataset of interleaved image-text documents

  • Paper: OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
  • Github: https://github.com/huggingface/OBELICS
  • Dataset: https://huggingface.co/datasets/HuggingFaceM4/OBELICS

Instruction Fine-tuning Datasets

Sensoriamento remoto

• MMRS-1M : Multi-sensor remote sensing instruction dataset
  • Paper: EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
  • Github: https://github.com/wivizhang/EarthGPT

Images + Videos

• VideoChat2-IT : Instruction fine-tuning dataset for images/videos
  • Paper: MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
  • Dataset: https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT

Visual Document Understanding

• InstructDoc : A dataset for zero-shot generalization of visual document understanding
  • Paper: InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions
  • Github: https://github.com/nttmdlab-nlp/InstructDoc
  • Dataset: https://github.com/nttmdlab-nlp/InstructDoc

Em geral

• ALLaVA-4V Data : The multimodal instruction fine-tuning dataset for the ALLaVA model
  • Paper: ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model
  • Github: https://github.com/FreedomIntelligence/ALLaVA
  • Dataset: https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V

Evaluation Datasets

Video Understanding

• MVBench : A comprehensive multi-modal video understanding benchmark
  • Paper: MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
  • Github: https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2
  • Dataset: https://huggingface.co/datasets/OpenGVLab/MVBench

Assunto

• OlympiadBench : An Olympiad-level bilingual multimodal scientific benchmark

  • Paper: OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
  • Github: https://github.com/OpenBMB/OlympiadBench
  • Dataset: https://github.com/OpenBMB/OlympiadBench

• MMMU : A new benchmark designed to evaluate MLLMs on massive multi-discipline tasks

  • Paper: MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
  • Github: https://github.com/MMMU-Benchmark/MMMU
  • Dataset: https://huggingface.co/datasets/MMMU/MMMU

Multitarefa

• MMT-Bench : A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask AGI
  • Paper: MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
  • Github: https://github.com/OpenGVLab/MMT-Bench
  • Dataset: https://huggingface.co/datasets/Kaining/MMT-Bench

Long Input

• MM-NIAH : The first benchmark specifically designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents
  • Paper: Needle In A Multimodal Haystack
  • Github: https://github.com/OpenGVLab/MM-NIAH
  • Dataset: https://github.com/OpenGVLab/MM-NIAH

Factuality

• MultiTrust : The first comprehensive and unified benchmark on the trustworthiness of MLLMs across five primary aspects: truthfulness, safety, robustness, fairness, and privacy
  • Paper: Benchmarking Trustworthiness of Multimodal Large Language Models: A Comprehensive Study
  • Github: https://github.com/thu-ml/MMTrustEval
  • Website: https://multi-trust.github.io/#leaderboard

Médico

• MultiMed : A benchmark designed to evaluate and enable large-scale learning across a wide spectrum of medical modalities and tasks

  • Paper: MultiMed: Massively Multimodal and Multitask Medical Understanding

• MedTrinity-25M : A large-scale multimodal dataset with multigranular annotations for medicine

  • Paper: MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
  • Github: https://github.com/UCSC-VLAA/MedTrinity-25M
  • Dataset: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M
  • Website: https://yunfeixie233.github.io/MedTrinity-25M/

Image Understanding

• MMIU : A comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks
  • Paper: MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
  • Github: https://github.com/OpenGVLab/MMIU
  • Dataset: https://huggingface.co/datasets/FanqingM/MMIU-Benchmark
  • Website: https://mmiu-bench.github.io/

Retrieval Augmented Generation (RAG) Datasets

• CRUD-RAG : A comprehensive Chinese benchmark for RAG

  • Paper: CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
  • Github: https://github.com/IAAR-Shanghai/CRUD_RAG
  • Dataset: https://github.com/IAAR-Shanghai/CRUD_RAG

• WikiEval : To do correlation analysis of difference metrics proposed in RAGAS

  • Paper: RAGAS: Automated Evaluation of Retrieval Augmented Generation
  • Github: https://github.com/explodinggradients/ragas
  • Dataset: https://huggingface.co/datasets/explodinggradients/WikiEval

• RGB : A benchmark for RAG

  • Paper: Benchmarking Large Language Models in Retrieval-Augmented Generation
  • Github: https://github.com/chen700564/RGB
  • Dataset: https://github.com/chen700564/RGB

• RAG-Instruct-Benchmark-Tester : An updated benchmarking test dataset for RAG use cases in the enterprise

  • Dataset: https://huggingface.co/datasets/llmware/rag_instruct_benchmark_tester
  • Website: https://medium.com/@darrenoberst/how-accurate-is-rag-8f0706281fd9

• ARES : An automated evaluation framework for RAG

  • Paper: ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
  • Github: https://github.com/stanford-futuredata/ARES
  • Dataset: https://github.com/stanford-futuredata/ARES

• ALCE : The quality assessment benchmark for context and responses

  • Paper: Enabling Large Language Models to Generate Text with Citations
  • Github: https://github.com/princeton-nlp/ALCE
  • Dataset: https://huggingface.co/datasets/princeton-nlp/ALCE-data

• CRAG : A comprehensive RAG benchmark

  • Paper: CRAG -- Comprehensive RAG Benchmark
  • Website: https://www.aicrowd.com/challenges/meta-comprehensive-rag-benchmark-kdd-cup-2024

• RAGEval ：A framework for automatically generating evaluation datasets to evaluate the knowledge usage ability of different LLMs in different scenarios

  • Paper: RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
  • Github: https://github.com/OpenBMB/RAGEval
  • Dataset: https://github.com/OpenBMB/RAGEval

• LFRQA ：A dataset of human-written long-form answers for cross-domain evaluation in RAG-QA systems

  • Paper: RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
  • Github: https://github.com/awslabs/rag-qa-arena

• MultiHop-RAG : Benchmarking retrieval-augmented generation for multi-hop queries

  • Paper: MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
  • Github: https://github.com/yixuantt/MultiHop-RAG/
  • Dataset: https://huggingface.co/datasets/yixuantt/MultiHopRAG

Contato

Informações de contato:

Lianwen Jin：[email protected]

Yang Liu：[email protected]

Due to our current limited human resources to manage such a vast amount of data resources, we regret that we are unable to include all data resources at this moment. If you find any important data resources that have not yet been included, we warmly invite you to submit relevant papers, data links, and other information to us. We will evaluate them, and if appropriate, we will include the data in the Awesome-LLMs-Datasets and the survey paper . Your assistance and support are greatly appreciated!

Citação

If you wish to cite this project, please use the following citation format:

 @article{liu2024survey,
  title={Datasets for Large Language Models: A Comprehensive Survey},
  author={Liu, Yang and Cao, Jiahuan and Liu, Chongyu and Ding, Kai and Jin, Lianwen},
  journal={arXiv preprint arXiv:2402.18041},
  year={2024}
}