Téléchargement Awesome LLMs Datasets - Téléchargement du code source Awesome LLMs Datasets

Awesome-llms-datasets

Résumez les ensembles de données de texte LLMS représentatifs existants sur cinq dimensions: les corpus pré-formation, les ensembles de données d'instructions à réglage fin, les ensembles de données de préférence, les ensembles de données d'évaluation et les ensembles de données NLP traditionnels . (Mises à jour régulières)
De nouvelles sections d'ensemble de données ont été ajoutées: ensembles de données multimodal de grands modèles de langage (MLLMS), ensembles de données de génération augmentée (RAG) de récupération . (Mises à jour progressives)

Papier

L'article "ensembles de données pour les modèles de grande langue: une enquête complète" a été publiée. （2024/2）

Abstrait:

Cet article se lance dans une exploration dans les ensembles de données du modèle grand langage (LLM), qui jouent un rôle crucial dans les progrès remarquables des LLM. Les ensembles de données sont l'infrastructure fondamentale analogue à un système racinaire qui soutient et nourrit le développement de LLMS. Par conséquent, l'examen de ces ensembles de données apparaît comme un sujet critique dans la recherche. Afin de répondre à l'absence actuelle d'un aperçu complet et d'une analyse approfondie des ensembles de données LLM, et pour obtenir un aperçu de leur statut actuel et de leurs tendances futures, cette enquête consolide et catégorise les aspects fondamentaux des ensembles de données LLM à partir de cinq perspectives: (1) Pre- corps de formation; (2) ensembles de données de réglage fin de l'instruction; (3) ensembles de données de préférence; (4) ensembles de données d'évaluation; (5) ensembles de données traditionnels de traitement du langage naturel (NLP). L'enquête met en lumière les défis dominants et souligne les voies potentielles pour une enquête future. De plus, une revue complète des ressources de données disponibles existantes est également fournie, y compris les statistiques de 444 ensembles de données, couvrant 8 catégories de langues et couvrant 32 domaines. Les informations de 20 dimensions sont incorporées dans les statistiques de l'ensemble de données. La taille totale des données interrogées dépasse 774,5 To pour les sociétés pré-formation et les instances de 700 m pour d'autres ensembles de données. Nous visons à présenter l'ensemble du paysage des ensembles de données de texte LLM, servant de référence complète pour les chercheurs dans ce domaine et contribuant à de futures études.

Fig 1. L'architecture globale de l'enquête. Zoomer pour une meilleure vue

Module d'information sur l'ensemble de données

Ce qui suit est un résumé du module d'information de l'ensemble de données.

Nom de corpus / jeu de données
Éditeur
Temps de libération
- «X» indique un mois inconnu.
Taille
Public ou pas
- «Tout» indique l'open source complète;
- «Partial» indique une source partiellement open;
- «Pas» indique non open source.
Licence
Langue
- «En» indique l'anglais;
- «ZH» indique le chinois;
- «AR» indique l'arabe;
- «ES» indique l'espagnol;
- «RU» indique le russe;
- «De» indique l'allemand;
- «KO» indique le coréen;
- «LT» indique le lituanien;
- «FA» indique le persan / farsi;
- «PL» indique le langage de programmation;
- «Multi» indique multilingue et le nombre entre parenthèses indique le nombre de langues incluses.
Méthode de construction
- «HG» indique un corpus / ensemble de données généré par l'homme;
- «MC» indique un corpus / ensemble de données construit par modèle;
- «CI» indique la collecte et l'amélioration du corpus / ensemble de données existant.
Catégorie
Source
Domaine
Catégorie d'instructions
Méthode d'évaluation des préférences
- «VO» indique le vote;
- «So» indique le tri;
- «SC» indique le score;
- «-H» indique des humains;
- «-M» indique des modèles.
Type de question
- «SQ» indique des questions subjectives;
- «OQ» indique des questions objectives;
- «Multi» indique plusieurs types de questions.
Méthode d'évaluation
- «CE» indique l'évaluation du code;
- «Il» indique une évaluation humaine;
- «Moi» indique l'évaluation du modèle.
Se concentrer
Nombres de catégories d'évaluation / sous-catégories
Catégorie d'évaluation
Nombre de catégories d'entités (tâche NER)
Nombre de catégories de relations (RE Task)

Changelog

（2024/01/17） Créez le référentiel de jeu de données Awesome-llms-datasets .
（2024/02/02） réviser les informations pour certains ensembles de données; Ajouter Dolma (corpus pré-formation | corpus généraux pré-formation | multi-catégories).
（2024/02/15） Ajouter la collection AYA (ensembles de données de réglage d'instructions | Instructions générales Fine Tuning DataSets | Hg & CI & MC); Ensemble de données AYA (Instructions Fineding DataSets | Instruction General Instruction Fined DataSets | Hg).
（2024/02/22） Ajouter OpenMathInstruct-1 (Instructions Fine Tuning DataSets | Finben (ensembles de données d'évaluation | Financial).
（2024/04/05）
- Ajouter de nouvelles sections de jeu de données: (1) ensembles de données de modèles de grande langue (MLLMS) multimodaux; (2) Retrouver des ensembles de données de génération augmentée (RAG) .
- Ajouter MMRS-1M (ensembles de données MLLMS | Instructions Fineding DataSets); Videochat2-it (ensembles de données MLLMS | Instructions Fineding DataSets); InstructDoc (ensembles de données MLLMS | Instructions Fineding DataSets); Données AllAVA-4V (ensembles de données MLLMS | Instructions Fineding DataSets); MVBench (ensembles de données MLLMS | ensembles de données d'évaluation); Olympiadbench (ensembles de données MLLMS | ensembles de données d'évaluation); MMMU (ensembles de données MLLMS | ensembles de données d'évaluation).
- Ajouter une série d'indices de référence (ensembles de données d'évaluation | plate-forme d'évaluation); OpenLLM Leadboard (ensembles de données d'évaluation | Plateforme d'évaluation); OpenCompass (ensembles de données d'évaluation | plate-forme d'évaluation); METB LEBERBOEL (ensembles de données d'évaluation | Plateforme d'évaluation); CA-MEDIFRE C-MTEB (ensembles de données d'évaluation | Plateforme d'évaluation).
- Ajouter NAH (aiguille dans un haystack) (ensembles de données d'évaluation | Texte long); Tooleyes (ensembles de données d'évaluation | Tool); UhGeval (ensembles de données d'évaluation | Factuality); Clongeval (ensembles de données d'évaluation | Texte long).
- Ajouter MathPile (corpus pré-entraînement | Corpora pré-formation spécifique au domaine); Wanjuan-CC (corpus pré-entraînement | Corparèmes généraux de formation pré-formation | Pages Web).
- Ajouter IEPILE (Instructions Fineding DataSets | Instruction General Instruction Fineding DataSets | CI); Instrugie (Instructions Fineding DataSets | Instructions General Instruction Fineding DataSets | Hg).
- Ajouter Crud-Rag (ensembles de données RAG); Wikieval (ensembles de données RAG); RVB (ensembles de données RAG); Rag-Instruct-Benchmark-Tester (ensembles de données RAG); ARES (ensembles de données RAG).
（2024/04/06）
- Ajouter GPQA (ensembles de données d'évaluation | Sujet); MGSM (ensembles de données d'évaluation | multilingue); HalueVal-Wild (ensembles de données d'évaluation | Factuality); CMATH (ensembles de données d'évaluation | Sujet); FineMath (ensembles de données d'évaluation | Sujet); QA en temps réel (ensembles de données d'évaluation | Factuality); Wyweb (ensembles de données d'évaluation | Sujet); ChineseFactEval (ensembles de données d'évaluation | factualité); Comptage-stars (ensembles de données d'évaluation | texte long).
- Ajouter Slimpajama (corpus pré-entraînement | corpus généraux pré-formation | multi-catégories); MassiveText (corpus pré-entraînement | corpus généraux de formation pré-formation | multi-catégories); MADLAD-400 (Corpora de pré-formation | Corpares généraux de formation pré-formation | Web); Minerva (corpus pré-entraînement | corpus généraux pré-formation | multi-catégories); CCALIGNED (Corporations de pré-formation | Corpares généraux de formation pré-entraînement | Corpus parallèle); Wikimatrix (corpus pré-entraînement | corpus généraux pré-formation | corpus parallèle); OpenWebmath (corpus pré-entraînement | corpus pré-formation spécifiques au domaine | mathématiques).
- Ajoutez des questions Web (ensembles de données NLP traditionnels | Réponse de questions | Connaissance QA).
- Ajouter ALCE (ensembles de données RAG).
- Ajouter AlphaFin (Instructions Fineding DataSets | COIG-CQIA (Instructions Fineing DataSets | Instructions General Instruction Fined DataSets | Hg & CI).
（2024/06/15）
- Ajouter un indice (ensembles de données d'évaluation | Medical); CHC-Bench (ensembles de données d'évaluation | Général); CIF-Bench (ensembles de données d'évaluation | Général); ACLUE (ensembles de données d'évaluation | Sujet); LESC (ensembles de données d'évaluation | NLU); AlignBench (ensembles de données d'évaluation | multitâche); ScikNoweval (ensembles de données d'évaluation | Sujet).
- Ajouter MAP-CC (corpus pré-formation | corpus généraux pré-formation | multi-catégories); Fineweb (corpus pré-formation | Corpora généraux de formation pré-formation | pages Web); CCI 2.0 (Corpora de pré-formation | Corparèmes généraux de pré-formation | Pages Web).
- Ajouter WildChat (ensembles de données de réglage fin de l'instruction | MC).
- Ajouter OpenHerSpReferences (ensembles de données de préférence | Sort); Huozi_rlhf_data (ensembles de données de préférence | vote); HelpSeter (ensembles de données de préférence | Score); HelpSteer2 (ensembles de données de préférence | Score).
- Ajouter un banc MMT (ensembles de données MLLMS | ensembles de données d'évaluation); Moscar (ensembles de données MLLMS | Corpora de pré-formation); MM-NIAH (ensembles de données MLLMS | ensembles de données d'évaluation).
- Ajouter Crag (ensembles de données RAG).
（2024/08/29）
- Ajouter GameBench (ensembles de données d'évaluation | Raisonnement); Halludial (ensembles de données d'évaluation | factualité); Wildbench (ensembles de données d'évaluation | Général); DomaineVal (ensembles de données d'évaluation | Code); Sysbench (ensembles de données d'évaluation | Général); Kobest (ensembles de données d'évaluation | NLU); SarcasmBench (ensembles de données d'évaluation | NLU); C ³ Banc (ensembles de données d'évaluation | Sujet); TableBench (ensembles de données d'évaluation | Raisonnement); ARableGaleval (ensembles de données d'évaluation | Loi).
- Ajouter des ensembles de données multitrust (MLLMS | ensembles de données d'évaluation); OBELISC (ensembles de données MLLMS | Corpora de pré-formation); Ensembles de données MLIMED (MLLMS | ensembles de données d'évaluation).
- Ajouter DCLM (Corpora de pré-formation | Corpora généraux de pré-formation | Pages Web).
- Ajouter le lituanien-QA-V1 (Instructions Fineding DataSets | CI & MC); Réinstruct (instructions de données de réglage fin | hg & ci & mc); Convisions de kollm (ensembles de données de réglage d'instructions | CI).
（2024/09/04）
- Ajouter LongWriter-6K (ensembles de données de réglage fin de l'instruction | CI & MC).
- Ajouter MedTrity-25m (ensembles de données MLLMS | ensembles de données d'évaluation); MMIU (ensembles de données MLLMS | ensembles de données d'évaluation).
- Ajouter un expositoire-prose-V1 (corpus de pré-formation | Corpares généraux de pré-formation | multi-catégories).
- Ajouter DebateQA (ensembles de données d'évaluation | Connaissance); NeedleBench (ensembles de données d'évaluation | Texte long); Arabicmmlu (ensembles de données d'évaluation | Sujet); Persianmmlu (ensembles de données d'évaluation | Sujet); TMMLU + (ensembles de données d'évaluation | Sujet).
- Ajouter RageVal (ensembles de données RAG); LFRQA (ensembles de données RAG); MultiHop-Rag (ensembles de données RAG).
Nous publierons les informations de l'ensemble de données au format CSV.

Table des matières

Corpus pré-formation
- Corpus de pré-formation généraux
  - Pages Web
  - Textes de langue
  - Livres
  - Matériel académique
  - Code
  - Corpus parallèle
  - Réseaux sociaux
  - Encyclopédie
  - Multi-catégories
- Corpus pré-formation spécifiques au domaine
  - Financier
  - Médical
  - Mathématiques
  - Autre
Ensembles de données de réglage fin de l'instruction
- Ensembles de données de réglage fin de l'instruction générale
  - Ensembles de données générés par l'homme (HG)
  - Ensembles de données construits par modèle (MC)
  - Collecte et amélioration des ensembles de données existants (IC)
  - HG & CI
  - HG et MC
  - CI et MC
  - HG & CI & MC
- Instructions spécifiques au domaine
  - Médical
  - Code
  - Légal
  - Mathématiques
  - Éducation
  - Autre
Ensembles de données de préférence
- Méthodes d'évaluation des préférences
  - Voter
  - Trier
  - Score
  - Autre
Ensembles de données d'évaluation
- Général
- Examen
- Sujet
- NLU
- Raisonnement
- Connaissance
- Texte long
- Outil
- Agent
- Code
- Dynamique
- Loi
- Médical
- Financier
- Normes sociales
- Factualité
- Évaluation
- Multitâche
- Multilingue
- Autre
- Plateforme d'évaluation
Ensembles de données NLP traditionnels
- Question Répondre
  - Compréhension de la lecture
    - Sélection et jugement
    - Test de cloze
    - Réponse extraction
    - QA sans restriction
  - Connaissance QA
  - Raisonnement QA
- Reconnaître l'impact textuel
- Mathématiques
- Résolution de coreférence
- Analyse des sentiments
- Correspondance sémantique
- Génération de texte
- Traduction de texte
- Résumé de texte
- Classification de texte
- Évaluation de la qualité du texte
- Texte à code
- Reconnaissance d'entité nommée
- Extraction de relation
- Multitâche
Ensembles de données de modèles de grande langue (MLLMS) multimodaux
- Corpus pré-formation
- Ensembles de données de réglage fin de l'instruction
- Ensembles de données d'évaluation
Ensembles de données de génération augmentée (RAG) de récupération

Corpus pré-formation

Les corpus pré-formation sont de grandes collections de données de texte utilisées pendant le processus de pré-formation des LLM.

Corpus de pré-formation généraux

Les corpus généraux pré-formation sont des ensembles de données à grande échelle composés de texte étendu à partir de divers domaines et sources. Leur principale caractéristique est que le contenu du texte ne se limite pas à un seul domaine, ce qui les rend plus adaptés à la formation de modèles fondamentaux généraux. Les corpus sont classés en fonction des catégories de données.

Format d'informations sur l'ensemble de données ：

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

Pages Web

CC-Stories 2018-6 | Pas | En | CI | Papier | Github | Ensemble de données
- Éditeur: Google Brain
- Taille: 31 Go
- Licence: -
- Source: Crawl commun
CC100 2020-7 | Tout | Multi (100) | CI | Papier | Ensemble de données
- Éditeur: Facebook AI
- Taille: 2,5 To
- Licence: termes d'utilisation communes de Crawl
- Source: Crawl commun
Cluecorpus2020 2020-3 | Tout | Zh | CI | Papier | Ensemble de données
- Éditeur: Organisation des indices
- Taille: 100 Go
- Licence: MIT
- Source: Crawl commun
Crawl commun 2007-x | Tout | Multi | HG | Site web
- Éditeur: Crawl commun
- Taille: -
- Licence: termes d'utilisation communes de Crawl
- Source: données de robottes Web
Culturax 2023-9 | Tout | Multi (167) | CI | Papier | Ensemble de données
- Éditeur: Université d'Oregon et al.
- Taille: 27 TB
- Licence: MC4 & Oscar Licence
- Source: MC4, Oscar
C4 2019-10 | Tout | En | CI | Papier | Ensemble de données
- Éditeur: Google Research
- Taille: 12,68 TB
- Licence: ODC-by & Common Crawl Conditions d'utilisation
- Source: Crawl commun
MC4 2021-6 | Tout | Multi (108) | CI | Papier | Ensemble de données
- Éditeur: Google Research
- Taille: 251 Go
- Licence: ODC-by & Common Crawl Conditions d'utilisation
- Source: Crawl commun
Oscar 22.01 2022-1 | Tout | Multi (151) | CI | Papier | Ensemble de données | Site web
- Éditeur: INRIA
- Taille: 8,41 TB
- Licence: CC0
- Source: Crawl commun
RealNews 2019-5 | Tout | En | CI | Papier | Github
- Éditeur: Université de Washington et al.
- Taille: 120 Go
- Licence: Apache-2.0
- Source: Crawl commun
Redpajama-v2 2023-10 | Tout | Multi (5) | CI | Github | Ensemble de données | Site web
- Éditeur: ordinateur ensemble
- Taille: 30,4 jetons T
- Licence: termes d'utilisation communes de Crawl
- Source: Crawl commun, C4, etc.
RefinedWeb 2023-6 | Partial | En | CI | Papier | Ensemble de données
- Éditeur: l'équipe Falcon LLM
- Taille: 5000 Go
- Licence: ODC-by-1.0
- Source: Crawl commun
Wudaocorpora-text 2021-6 | Partial | Zh | HG | Papier | Ensemble de données
- Éditeur: Baai et al.
- Taille: 200 Go
- Licence: MIT
- Source: pages Web chinoises
Wanjuan-CC 2024-2 | Partial | En | HG | Papier | Ensemble de données
- Éditeur: Shanghai Artifcial Intelligence Laboratory
- Taille: 1 jetons T
- Licence: CC-BY-4.0
- Source: Crawl commun
Madlad-400 2023-9 | Tout | Multi (419) | HG | Papier | Github | Ensemble de données
- Éditeur: Google Deepmind et al.
- Taille: 2,8 tokens
- Licence: ODL-BY
- Source: Crawl commun
Fineweb 2024-4 | Tout | En | CI | Ensemble de données
- Éditeur: HuggingFacefw
- Taille: jetons de 15 To
- Licence: ODC-by-1.0
- Source: Crawl commun
CCI 2.0 2024-4 | Tout | Zh | HG | Ensemble de données1 | Ensemble de données2
- Éditeur: baai
- Taille: 501 Go
- Licence: Agggelage de l'utilisation du CCI
- Source: pages Web chinoises
DCLM 2024-6 | Tout | En | CI | Papier | Github | Ensemble de données | Site web
- Éditeur: Université de Washington et al.
- Taille: 279.6 TB
- Licence: termes d'utilisation communes de Crawl
- Source: Crawl commun

Textes de langue

ANC 2003-X | Tout | En | HG | Site web
- Éditeur: la US National Science Foundation et al.
- Taille: -
- Licence: -
- Source: Textes anglais américain
BNC 1994-X | Tout | En | HG | Site web
- Éditeur: Oxford University Press et al.
- Taille: 4124 textes
- Licence: -
- Source: Textes anglais britanniques
News-Crawl 2019-1 | Tout | Multi (59) | HG | Ensemble de données
- Éditeur: Ukri et al.
- Taille: 110 Go
- Licence: CC0
- Source: journaux

Livres

Archive d'Anna 2023-X | Tout | Multi | HG | Site web
- Éditeur: Anna
- Taille: 586,3 To
- Licence: -
- Source: Sci-Hub, Bibliothèque Genèse, Z-Library, etc.
BookCorpusopen 2021-5 | Tout | En | CI | Papier | Github | Ensemble de données
- Éditeur: Jack Bandy et al.
- Taille: 17 868 livres
- Licence: Smashwords Conditions d'utilisation
- Source: Toronto Book Corpus
PG-19 2019-11 | Tout | En | HG | Papier | Github | Ensemble de données
- Éditeur: DeepMind
- Taille: 11,74 Go
- Licence: Apache-2.0
- Source: Project Gutenberg
Projet Gutenberg 1971-X | Tout | Multi | HG | Site web
- Éditeur: Ibiblio et al.
- Taille: -
- Licence: le projet Gutenberg
- Source: données électroniques
Smashwords 2008-x | Tout | Multi | HG | Site web
- Éditeur: Draft2digital et al.
- Taille: -
- Licence: Smashwords Conditions d'utilisation
- Source: données électroniques
Toronto Book Corpus 2015-6 | Pas | En | HG | Papier | Site web
- Éditeur: Université de Toronto et al.
- Taille: 11 038 livres
- Licence: MIT & Smashwords Conditions d'utilisation
- Source: Smashwords

Matériel académique

Arxiv 1991-X | Tout | En | HG | Site web
- Éditeur: Paul Ginsparg et al.
- Taille: -
- Licence: Conditions d'utilisation des API ARXIV
- Source: ARXIV Preprint
S2ORC 2020-6 | Tout | En | CI | Papier | Github
- Éditeur: AI2 et al.
- Taille: 81,1 Mo
- Licence: ODC-by-1.0
- Source: Mag, Arxiv, PubMed, etc.

Code

BigQuery 2022-3 | Pas | PL | CI | Papier | Github
- Éditeur: Salesforce Research
- Taille: 341.1 Go
- Licence: Apache-2.0
- Source: BigQuery
GitHub 2008-4 | Tout | PL | HG | Site web
- Éditeur: Microsoft
- Taille: -
- Licence: -
- Source: Divers projets de code
Phi-1 2023-6 | Pas | En & pl | HG & MC | Papier | Ensemble de données
- Éditeur: Microsoft Research
- Taille: 7 b jetons
- Licence: CC-BY-NC-SA-3.0
- Source: la pile, stackOverflow, GPT-3.5 Génération
La pile 2022-11 | Tout | PL (358) | HG | Papier | Ensemble de données
- Éditeur: ServiceNow Research et al.
- Taille: 6 To
- Licence: les termes des licences originales
- Source: fichiers de code source autorisés avec permissivement

Corpus parallèle

MTP 2023-9 | Tout | En & zh | HG & CI | Ensemble de données
- Éditeur: baai
- Taille: 1,3 To
- Licence: Protocole d'utilisation des données BAAI
- Source: Paies de texte parallèle chinois-anglais sur le Web
Multiun 2010-5 | Tout | Multi (7) | HG | Papier | Site web
- Éditeur: Centre de recherche allemand pour l'intelligence artificielle (DFKI) GmbH
- Taille: 4353 Mo
- Licence: -
- Source: Documents des Nations Unies
Paracrawl 2020-7 | Tout | Multi (42) | HG | Papier | Site web
- Éditeur: Promppsit et al.
- Taille: 59996 fichiers
- Licence: CC0
- Source: données de robottes Web
Uncorpus v1.0 2016-5 | Tout | Multi (6) | HG | Papier | Site web
- L'éditeur: Nations Unies et al.
- Taille: fichiers 799276
- Licence: -
- Source: Documents des Nations Unies
CCALIGNED 2020-11 | Tout | Multi (138) | HG | Papier | Ensemble de données
- Éditeur: Facebook Ai et al.
- Taille: paires d'URL de 392 m
- Licence: -
- Source: Crawl commun
Wikimatrix 2021-4 | Tout | Multi (85) | HG | Papier | Github | Ensemble de données
- Éditeur: Facebook Ai et al.
- Taille: 134 m de phrases parallèles
- Licence: cc-by-sa
- Source: Wikipedia

Réseaux sociaux

OpenWebText 2019-4 | Tout | En | HG | Site web
- Éditeur: Brown University
- Taille: 38 Go
- Licence: CC0
- Source: Reddit
Pushhift Reddit 2020-1 | Tout | En | CI | Papier | Site web
- Éditeur: Pushshift.io et al.
- Taille: 2 To
- Licence: -
- Source: Reddit
Reddit 2005-6 | Tout | En | HG | Site web
- Éditeur: Condé Nast Digital et al.
- Taille: -
- Licence: -
- Source: publications sur les réseaux sociaux
Stackexchange 2008-9 | Tout | En | HG | Ensemble de données | Site web
- Éditeur: Exchange de pile
- Taille: -
- Licence: CC-by-sa-4.0
- Source: données de questions et réponses de la communauté
WebText 2019-2 | Partial | En | HG | Papier | Github | Ensemble de données
- Éditeur: Openai
- Taille: 40 Go
- Licence: MIT
- Source: Reddit
Zhihu 2011-1 | Tout | Zh | HG | Site web
- Éditeur: Pékin Zhizhe Tianxia Technology Co., Ltd
- Taille: -
- Licence: Contrat utilisateur de Zhihu
- Source: publications sur les réseaux sociaux

Encyclopédie

Baidu Baike 2008-4 | Tout | Zh | HG | Site web
- Éditeur: Baidu
- Taille: -
- Licence: Contrat utilisateur de Baidu Baike
- Source: données de contenu encyclopédique
Tigerbot-wiki 2023-5 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 205 Mo
- Licence: Apache-2.0
- Source: baidu baike
Wikipedia 2001-1 | Tout | Multi | HG | Ensemble de données | Site web
- Éditeur: Fondation Wikimedia
- Taille: -
- Licence: CC-by-sa-3.0 & GFDL
- Source: données de contenu encyclopédique

Multi-catégories

ArabiicText 2022 2022-12 | Tout | Ar | HG & CI | Ensemble de données
- Éditeur: Baai et al.
- Taille: 201,9 Go
- Licence: CC-by-sa-4.0
- Source: ArabicWeb, Oscar, CC100, etc.
MNBVC 2023-1 | Tout | Zh | HG & CI | Github | Ensemble de données
- Éditeur: communauté Liwu
- Taille: 20811 Go
- Licence: MIT
- Source: livres chinois, pages Web, thèses, etc.
Redpajama-v1 2023-4 | Tout | Multi | HG & CI | Github | Ensemble de données
- Éditeur: ordinateur ensemble
- Taille: 1,2 jetons T
- Licence: -
- Source: Crawl commun, github, livres, etc.
Roots 2023-3 | Partial | Multi (59) | HG & CI | Papier | Ensemble de données
- Éditeur: Hugging Face et al.
- Taille: 1,61 TB
- Licence: Bloom Open-Rail-M
- Source: Oscar, github, etc.
La pile 2021-1 | Tout | En | HG & CI | Papier | Github | Ensemble de données
- Éditeur: Eleutherai
- Taille: 825.18 Go
- Licence: MIT
- Source: Livres, Arxiv, Github, etc.
TIGERBOT_PRETRAIN_EN 2023-5 | Partial | En | CI | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 51 Go
- Licence: Apache-2.0
- Source: Livres anglais, pages Web, en-wiki, etc.
TIGERBOT_PRETRAIN_ZH 2023-5 | Partial | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 55 Go
- Licence: Apache-2.0
- Source: Livres chinois, pages Web, Zh-Wiki, etc.
Wanjuantext-1.0 2023-8 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Laboratoire Shanghai AI
- Taille: 1094 Go
- Licence: CC-BY-4.0
- Source: pages Web, encyclopédie, livres, etc.
Dolma 2024-1 | Tout | En | HG & CI | Papier | Github | Ensemble de données
- Éditeur: AI2 et al.
- Taille: 11519 Go
- Licence: MR Accord
- Source: Project Gutenberg, C4, Reddit, etc.
Slimpajama 2023-6 | Tout | En | HG & CI | Github | Ensemble de données | Site web
- Éditeur: Cerebras et al.
- Taille: jetons 627 B
- Licence: -
- Source: Crawl commun, C4, Github, etc.
MassiveText 2021-12 | Pas | Multi | HG & CI | Papier
- Éditeur: Google Deepmind
- Taille: 10,5 To
- Licence: -
- Source: MassiveWeb, C4, livres, etc.
Minerva 2022-6 | Pas | En | HG | Papier
- Éditeur: Google Research
- Taille: jetons 38,5 b
- Licence: -
- Source: Arxiv, pages Web, etc.
MAP-CC 2024-4 | Tout | Zh | HG | Papier | Github | Ensemble de données | Site web
- Éditeur: Communauté de recherche multimodale de la projection d'art et al.
- Taille: 840,48 B
- Licence: CC-BY-NC-ND-4.0
- Source: Crawl commun chinois, encyclopédies chinoises, livres chinois, etc.
Expository-prose-v1 2024-8 | Tout | En | HG & CI | Papier | Github | Ensemble de données
- Éditeur: Pints.ai Labs
- Taille: jetons 56 B
- Licence: MIT
- Source: Arxiv, Wikipedia, Gutenberg, etc.

Corpus pré-formation spécifiques au domaine

Les sociétés de pré-formation spécifiques au domaine sont des ensembles de données LLM personnalisés pour des champs ou des sujets spécifiques. Le type de corpus est généralement utilisé dans la phase de pré-formation incrémentielle des LLM. Les corpus sont classés en fonction des domaines de données.

Format d'informations sur l'ensemble de données ：

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

Financier

BBT-Fincorpus 2023-2 | Partial | Zh | HG | Papier | Github | Site web
- Éditeur: Fudan University et al.
- Taille: 256 Go
- Licence: -
- Source: Annonces de l'entreprise, rapports de recherche, financier
- Catégorie: Multi
- Domaine: financement
FinCorpus 2023-9 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: du Xiaoman
- Taille: 60,36 Go
- Licence: Apache-2.0
- Source: Annonces de l'entreprise, nouvelles financières, questions d'examen financier
- Catégorie: Multi
- Domaine: financement
Finglm 2023-7 | Tout | Zh | HG | Github
- Éditeur: Knowledge Atlas et al.
- Taille: 69 Go
- Licence: Apache-2.0
- Source: Rapports annuels des sociétés cotées
- Catégorie: Textes linguistiques
- Domaine: financement
TIGERBOT-GARDING 2023-5 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 488 Mo
- Licence: Apache-2.0
- Source: Rapports financiers
- Catégorie: Textes linguistiques
- Domaine: financement
Tigerbot-Research 2023-5 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 696 Mo
- Licence: Apache-2.0
- Source: rapports de recherche
- Catégorie: Textes linguistiques
- Domaine: financement

Médical

Medical-PT 2023-5 | Tout | Zh | CI | Github | Ensemble de données
- Éditeur: Ming Xu
- Taille: 632,78 Mo
- Licence: Apache-2.0
- Source: données sur l'encyclopédie médicale, manuels médicaux
- Catégorie: Multi
- Domaine: médical
PubMed Central 2000-2 | Tout | En | HG | Site web
- Éditeur: NCBI
- Taille: -
- Licence: avis de droit d'auteur PMC
- Source: littérature scientifique biomédicale
- Catégorie: matériel académique
- Domaine: médical

Mathématiques

Preuve-pile-2 2023-10 | Tout | En | HG & CI | Papier | Github | Ensemble de données | Site web
- Éditeur: Princeton University et al.
- Taille: jetons 55 B
- Licence: -
- Source: Arxiv, OpenWebmath, Algebraicstack
- Catégorie: Multi
- Domaine: mathématiques
Mathpile 2023-12 | Tout | En | HG | Papier | Github | Ensemble de données
- Éditeur: Shanghai Jiao Tong University et al.
- Taille: jetons 9,5 b
- Licence: CC-BY-NC-SA-4.0
- Source: manuels, Wikipedia, Proofwiki, Commoncrawl, StacKexchange, Arxiv
- Catégorie: Multi
- Domaine: mathématiques
OpenWebmath 2023-10 | Tout | En | HG | Papier | Github | Ensemble de données
- Éditeur: Université de Toronto et al.
- Taille: jetons 14,7 b
- Licence: ODC-by-1.0
- Source: Crawl commun
- Catégorie: pages Web
- Domaine: mathématiques

Autre

Tigerbot-Law 2023-5 | Tout | Zh | HG | Papier | Github | Ensemble de données
- Éditeur: Tigerbot
- Taille: 29,9 Mo
- Licence: Apache-2.0
- Source: clauses légales
- Catégorie: Textes linguistiques
- Domaine: loi
Transgpt-PT 2023-7 | Tout | Zh | HG | Github | Ensemble de données
- Éditeur: Université de Jiaotong de Pékin
- Taille: 35,8 Mo
- Licence: Apache-2.0
- Source: Documents technologiques, informations sur la construction d'ingénierie, données statistiques, etc.
- Catégorie: Multi
- Domaine: transport

Ensembles de données de réglage fin de l'instruction

Les ensembles de données de réglage fin des instructions se compose d'une série de paires de texte comprenant des «entrées d'instructions» et des «sorties de réponse». Les «entrées d'instruction» représentent les demandes faites par les humains au modèle. Il existe différents types d'instructions, telles que la classification, le résumé, la paraphrasage, etc. «Réponse les sorties» sont les réponses générées par le modèle suivant l'instruction et s'alignement sur les attentes humaines.

Ensembles de données de réglage fin de l'instruction générale

Les ensembles de données de réglage fin des instructions contiennent une ou plusieurs catégories d'instructions sans restrictions de domaine, visant principalement à améliorer la capacité de suivi des instructions des LLM dans les tâches générales. Les ensembles de données sont classés en fonction des méthodes de construction.

Format d'informations sur l'ensemble de données ：

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

Ensembles de données générés par l'homme (HG)

Databricks-Dolly-15k 2023-4 | Tout | En | HG | Ensemble de données | Site web
- Éditeur: Databricks
- Taille: 15011 instances
- Licence: CC-by-sa-3.0
- Source: générée manuellement en fonction de différentes catégories d'instructions
- Catégorie d'instructions: Multi
Instructionwild_v2 2023-6 | Tout | En & zh | HG | Github
- Éditeur: Université nationale de Singapour
- Taille: 110k instances
- Licence: -
- Source: collecté sur le Web
- Catégorie d'instructions: Multi
LCCC 2020-8 | Tout | Zh | HG | Papier | Github
- Éditeur: Tsinghua University et al.
- Taille: 12m instances
- Licence: MIT
- Source: Crawl Interactions des utilisateurs sur les réseaux sociaux
- Catégorie d'instructions: Multi
OASST1 2023-4 | Tout | Multi (35) | HG | Papier | Github | Ensemble de données
- Éditeur: openassistant
- Taille: 161443 Instances
- Licence: Apache-2.0
- Source: générée et annotée par les humains
- Catégorie d'instructions: Multi
OL-CC 2023-6 | Tout | Zh | HG | Ensemble de données
- Éditeur: baai
- Taille: 11655 instances
- Licence: Apache-2.0
- Source: générée et annotée par les humains
- Catégorie d'instructions: Multi
Zhihu-kol 2023-3 | Tout | Zh | HG | Github | Ensemble de données
- Éditeur: Wangrui6
- Taille: 1006218 Instances
- Licence: MIT
- Source: ramper de Zhihu
- Catégorie d'instructions: Multi
Ensemble de données AYA 2024-2 | Tout | Multi (65) | HG | Papier | Ensemble de données | Site web
- Éditeur: Cohere for AI Community et al.
- Taille: 204k instances
- Licence: Apache-2.0
- Source: collecté manuellement et annoté via la plate-forme d'annotation AYA
- Catégorie d'instructions: Multi
Instrutie 2023-5 | Tout | En & zh | HG | Papier | Github | Ensemble de données
- Éditeur: Zhejiang University et al.
- Taille: 371700 Instances
- Licence: MIT
- Source: Baidu Baike, Wikipedia
- Catégorie d'instructions: Extraction

Ensembles de données construits par modèle (MC)

Alpaca_data 2023-3 | Tout | En | MC | Github
- Éditeur: Stanford Alpaca
- Taille: 52k instances
- Licence: Apache-2.0
- Source: Généré par Text-Davinci-003 avec des invites aplaca_data
- Catégorie d'instructions: Multi
Belle_Generated_Chat 2023-5 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 396004 Instances
- Licence: GPL-3.0
- Source: générée par Chatgpt
- Catégorie d'instructions: Génération
Belle_Multiturn_Chat 2023-5 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 831036 Instances
- Licence: GPL-3.0
- Source: générée par Chatgpt
- Catégorie d'instructions: Multi
Belle_train_0.5m_cn 2023-4 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 519255 Instances
- Licence: GPL-3.0
- Source: Généré par Text-Davinci-003
- Catégorie d'instructions: Multi
Belle_train_1m_cn 2023-4 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 917424 Instances
- Licence: GPL-3.0
- Source: Généré par Text-Davinci-003
- Catégorie d'instructions: Multi
Belle_train_2m_cn 2023-5 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 2m instances
- Licence: GPL-3.0
- Source: générée par Chatgpt
- Catégorie d'instructions: Multi
Belle_train_3.5m_cn 2023-5 | Tout | Zh | MC | Github | Ensemble de données
- Éditeur: Belle
- Taille: 3606402 Instances
- Licence: GPL-3.0
- Source: générée par Chatgpt
- Catégorie d'instructions: Multi
Camel 2023-3 | Tout | Multi & PL | MC | Papier | Github | Ensemble de données | Site web
- Éditeur: Kaust
- Taille: 1659328 Instances
- Licence: CC-BY-NC-4.0
- Source: Dialogue généré par deux agents GPT-3.5-turbo
- Catégorie d'instructions: Multi
Chatppt_corpus 2023-6 | Tout | Zh | MC | Github
- Éditeur: Plexpt
- Taille: 3270k instances
- Licence: GPL-3.0
- Source: générée par GPT-3.5-turbo
- Catégorie d'instructions: Multi
Instructionwild_v1 2023-3 | Tout | En & zh | MC | Github
- Éditeur: Université nationale de Singapour
- Taille: 104k instances
- Licence: -
- Source: générée par l'API OpenAI
- Catégorie d'instructions: Multi
LMSYS-CHAT-1M 2023-9 | Tout | Multi | MC | Papier | Ensemble de données
- Éditeur: UC Berkeley et al.
- Taille: 1M Instances
- Licence: Licence LMSYS-CHAT-1M
- Source: générée par plusieurs LLM
- Catégorie d'instructions: Multi
MOSS_002_SFT_DATA 2023-4 | Tout | En & zh | MC | Github | Ensemble de données
- Éditeur: Université Fudan
- Taille: 1161137 Instances
- Licence: CC-BY-NC-4.0
- Source: Généré par Text-Davinci-003
- Catégorie d'instructions: Multi
MOSS_003_SFT_DATA 2023-4 | Tout | En & zh | MC | Github | Ensemble de données
- Éditeur: Université Fudan
- Taille: 1074551 Instances
- Licence: CC-BY-NC-4.0
- Source: données de conversation de MOSS-002 et générées par GPT-3.5-turbo
- Catégorie d'instructions: Multi
MOSS_003_SFT_PLUGIN_DATA 2023-4 | Partial | En & zh | MC | Github | Ensemble de données
- Éditeur: Université Fudan
- Taille: 300k instances
- Licence: CC-BY-NC-4.0
- Source: générée par les plugins et les LLM
- Catégorie d'instructions: Multi
Openchat 2023-7 | Tout | En | MC | Papier | Github | Ensemble de données
- Éditeur: Tsinghua University et al.
- Taille: instances 70k
- Licence: MIT
- Source: Sharegpt
- Catégorie d'instructions: Multi
Redgpt-dataset-v1-cn 2023-4 | Partial | Zh | MC | Github
- Éditeur: Da-Southampton
- Taille: Instances 50K
- Licence: Apache-2.0
- Source: générée par LLMS
- Catégorie d'instructions: Multi
Auto-instruction 2022-12 | Tout | En | MC | Papier | Github
- Éditeur: Université de Washington et al.
- Taille: 52445 Instances
- Licence: Apache-2.0
- Source: générée par GPT-3
- Catégorie d'instructions: Multi
Sharechat 2023-4 | Tout | Multi | MC | Site web
- Éditeur: Sharechat
- Taille: 90k instances
- Licence: CC0
- Source: Sharegpt
- Catégorie d'instructions: Multi
Sharegpt-chinois-anglais-90k 2023-7 | Tout | En & zh | MC | Github | Ensemble de données
- Éditeur: Shareai
- Taille: 90k instances
- Licence: Apache-2.0
- Source: Sharegpt
- Catégorie d'instructions: Multi
Sharegpt90k 2023-4 | Tout | En | MC | Ensemble de données
- Éditeur: Ryokoai
- Taille: 90k instances
- Licence: CC0
- Source: Sharegpt
- Catégorie d'instructions: Multi
Ultrachat 2023-5 | Tout | En | MC | Papier | Github
- Éditeur: Université Tsinghua
- Taille: 1468352 Instances
- Licence: CC-BY-NC-4.0
- Source: Dialogue généré par deux agents de Chatgpt
- Catégorie d'instructions: Multi
Instructions non naturelles 2022-12 | Tout | En | MC | Papier | Github
- Éditeur: Tel Aviv University et al.
- Taille: 240670 Instances
- Licence: MIT
- Source: générée par LLMS
- Catégorie d'instructions: Multi
WebGLM-QA 2023-6 | Tout | En | MC | Papier | Github | Ensemble de données
- Éditeur: Tsinghua University et al.
- Taille: 44979 instances
- Licence: Apache-2.0
- Source: Construire WebGLM-QA via Bootstrapage LLM dans le contexte
- Catégorie d'instructions: Open QA
Wizard_EVOL_INSTRUCT_196K 2023-6 | Tout | En | MC | Papier | Github | Ensemble de données
- Éditeur: Microsoft et al.
- Taille: 196K Instances
- Licence: -
- Source: Instructions d'évolution à travers la méthode Evol-Instruct
- Catégorie d'instructions: Multi
Wizard_EVOL_INSTRUCT_70K 2023-5 | Tout | En | MC | Papier | Github | Ensemble de données
- Éditeur: Microsoft et al.
- Taille: instances 70k
- Licence: -
- Source: Instructions d'évolution à travers la méthode Evol-Instruct
- Catégorie d'instructions: Multi
Wildchat 2024-5 | Partial | Multi | MC | Papier | Ensemble de données
- Éditeur: Cornell University et al.
- Taille: 1039785 Instances
- Licence: Licence d'impact AI2
- Source: Conversations entre les utilisateurs et Chatgpt, GPT-4
- Catégorie d'instructions: Multi

Collecte et amélioration des ensembles de données existants (IC)

CrossFit 2021-4 | Tout | En | CI | Papier | Github
- Éditeur: Université de Californie du Sud
- Taille: 269 ensembles de données
- Licence: -
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Dialogstudio 2023-7 | Tout | En | CI | Papier | Github | Ensemble de données
- Éditeur: Salesforce AI et al.
- Taille: 87 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Dynosaur 2023-5 | Tout | En | CI | Papier | Github | Ensemble de données | Site web
- Éditeur: UCLA et al.
- Taille: 801900 Instances
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Flan-Mini 2023-7 | Tout | En | CI | Papier | Github | Ensemble de données
- Éditeur: Singapour University of Technology and Design
- Taille: 1,34 m instances
- Licence: CC
- Source: Collection et amélioration de divers ensembles de données de réglage des instructions
- Catégorie d'instructions: Multi
Flan 2021 2021-9 | Tout | Multi | CI | Papier | Github
- Éditeur: Google Research
- Taille: 62 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Flan 2022 2023-1 | Partial | Multi | CI | Papier | Github | Ensemble de données
- Éditeur: Google Research
- Taille: 1836 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données de réglage des instructions
- Catégorie d'instructions: Multi
InstructDial 2022-5 | Tout | En | CI | Papier | Github
- Éditeur: Université Carnegie Mellon
- Taille: 59 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Instructions naturelles 2021-4 | Tout | En | CI | Papier | Github | Ensemble de données
- Éditeur: Allen Institute pour AI et al.
- Taille: 61 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
OIG 2023-3 | Tout | En | CI | Ensemble de données
- Éditeur: laion
- Taille: 3878622 Instances
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données
- Catégorie d'instructions: Multi
Platypus ouvert 2023-8 | Tout | En | CI | Papier | Github | Ensemble de données | Site web
- Éditeur: Université de Boston
- Taille: 24926 instances
- Licence: -
- Source: Collection et amélioration de divers ensembles de données
- Catégorie d'instructions: Multi
Banc Opt-IML 2022-12 | Pas | Multi | CI | Papier | Github
- Éditeur: Meta Ai
- Taille: 2000 ensembles de données
- Licence: MIT
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Invitsource 2022-2 | Tout | En | CI | Papier | Github
- Éditeur: Brown University et al.
- Taille: 176 ensembles de données
- Licence: Apache-2.0
- Source: Collection et amélioration de divers ensembles de données NLP
- Catégorie d'instructions: Multi
Instructions super-naturelles 2022-4 | Tout | Multi | CI | Papier | Github
- Éditeur: Univ. de Washington et al.
- Taille: 1616 ensembles de données
- Licence: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
T0 2021-10 | Tout | 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 | Tout | 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 | Tout | 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 | Tout | EN & ZH | CI | Paper | Github | Ensemble de données
- 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 | Tout | KO | CI | Ensemble de données
- Publisher: davidkim205
- Size: 1122566 instances
- License: Apache-2.0
- Source: Collection and improvement of Korean datasets
- Instruction Category: Multi

HG & CI

Firefly 2023-4 | Tout | ZH | HG & CI | Github | Ensemble de données
- 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 | Tout | EN | HG & CI | Paper | Ensemble de données
- 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 | Tout | ZH | HG & CI | Paper | Ensemble de données
- 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 | Papier
- Publisher: OpenAI
- Size: 14378 instances
- License: -
- Source: Platform Q&A data and manual labeling
- Instruction Category: Multi

CI & MC

Alpaca_GPT4_data 2023-4 | Tout | 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 | Tout | ZH | CI & MC | Github | Ensemble de données
- 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 | Tout | Multi (52) | CI & MC | Paper | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Tout | EN | CI & MC | Paper | Github | Ensemble de données
- 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 | Tout | Multi | CI & MC | Dataset | Site web
- 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 | Tout | EN | CI & MC | Paper | Github | Ensemble de données
- 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 | Tout | EN & ZH | CI & MC | Paper | Github | Ensemble de données
- 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 | Tout | EN | CI & MC | Paper | Github | Ensemble de données
- 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 | Tout | EN & ZH | CI & MC | Github | Ensemble de données
- 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 | Tout | Multi | CI & MC | Paper | Ensemble de données
- 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 | Tout | ZH | CI & MC | Github | Ensemble de données
- 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 | Tout | LT | CI & MC | Paper | Ensemble de données
- 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 | Tout | EN & ZH | CI & MC | Paper | Github | Ensemble de données
- 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 | Tout | ZH | HG & CI & MC | Paper | Github | Ensemble de données
- Publisher: BAAI
- Size: 191191 instances
- License: Apache-2.0
- Source: Translated instructions, Leetcode, Chinese exams, etc.
- Instruction Category: Multi
HC3 2023-1 | Tout | 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 | Tout | Multi | HG & CI & MC | Paper | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Tout | Multi (114) | HG & CI & MC | Paper | Dataset | Site web
- 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:

Médical

ChatDoctor 2023-3 | Tout | EN | HG & MC | Paper | Github | Ensemble de données
- 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 | Tout | ZH | MC | Github | Ensemble de données
- 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 | Tout | ZH | HG | Paper | Github | Ensemble de données
- 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 | Tout | ZH | HG & CI | Paper | Github | Dataset | Site web
- 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 | Tout | ZH | HG & MC | Paper | Github | Ensemble de données
- 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 | Tout | 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 | Tout | EN | HG & CI | Paper | Github | Ensemble de données
- 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 | Tout | EN & ZH | CI | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Tout | ZH | MC | Github | Ensemble de données
- Publisher: michael-wzhu
- Size: 112565 instances
- License: Apache-2.0
- Source: Generated by ChatGPT
- Instruction Category: Multi
- Domain: Medical

Code

Code_Alpaca_20K 2023-3 | Tout | EN & PL | MC | Github | Ensemble de données
- Publisher: Sahil Chaudhary
- Size: 20K instances
- License: Apache-2.0
- Source: Generated by Text-Davinci-003
- Instruction Category: Code
- Domain: Code
CodeContest 2022-3 | Tout | 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 | Tout | EN & PL (277) | HG | Paper | Github | Ensemble de données
- Publisher: Bigcode
- Size: 702062 instances
- License: MIT
- Source: GitHub Action dump
- Instruction Category: Code
- Domain: Code
ToolAlpaca 2023-6 | Tout | 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 | Tout | 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

Légal

DISC-Law-SFT 2023-9 | Partial | ZH | HG & CI & MC | Paper | Github | Site web
- 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 | Tout | ZH | - | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- Publisher: Peking Universit
- Size: 21476 instances
- License: Apache-2.0
- Source: Generated by ChatGPT with other datasets' prompts
- Instruction Category: Multi
- Domain: Law

Mathématiques

BELLE_School_Math 2023-5 | Tout | ZH | MC | Github | Ensemble de données
- Publisher: BELLE
- Size: 248481 instances
- License: GPL-3.0
- Source: Generated by ChatGPT
- Instruction Category: Math
- Domain: Math
Goat 2023-5 | Tout | EN | HG | Paper | Github | Ensemble de données
- Publisher: National University of Singapore
- Size: 1746300 instances
- License: Apache-2.0
- Source: Artificially synthesized data
- Instruction Category: Math
- Domain: Math
MWP 2021-9 | Tout | EN & ZH | CI | Paper | Github | Ensemble de données
- 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 | Ensemble de données
- 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

Éducation

Child_chat_data 2023-8 | Tout | 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 | Ensemble de données
- 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 | Tout | ZH | HG & CI | Github | Ensemble de données
- 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

Autre

DISC-Fin-SFT 2023-10 | Partial | ZH | HG & CI & MC | Paper | Github | Site web
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Tout | ZH | HG | Github | Ensemble de données
- 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:

Voter

Chatbot_arena_conversations 2023-6 | All | Multi | HG & MC | Paper | Ensemble de données
- 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 | Ensemble de données
- 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 web
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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

Trier

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

Score

Stack-Exchange-Preferences 2021-12 | All | EN | HG | Paper | Ensemble de données
- 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 | Ensemble de données
- 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 | Tout | EN | CI & MC | Paper | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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

Autre

Medical-rlhf 2023-5 | All | ZH | CI & MC | Github | Ensemble de données
- 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:

Général

AlpacaEval 2023-5 | All | EN | CI & MC | Paper | Github | Dataset | Site web
- 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 | Ensemble de données
- 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 web
- 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 web
- 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 web
- 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 web
- 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 web
- 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 | Tout | EN | HG | Paper | Github | Ensemble de données
- 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

Examen

AGIEval 2023-4 | All | EN & ZH | HG & CI | Paper | Github | Ensemble de données
- 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

Sujet

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 web
- Publisher: Tianjin University
- Taille: -
- 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 web
- 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 web
- 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 web
- 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 | Ensemble de données
- 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 web
- 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 | Tout | EN | HG | Paper | Github | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Ensemble de données
- 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 | Papier
- 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 web
- 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 | Dataset
- 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 | Dataset
- 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 | Papier
- 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 | Dataset
- 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 | Tout | FA | HG | Paper | Dataset
- 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 | Dataset
- 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 web
- 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 web
- 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 web
- 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 | Tout | 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 web
- 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 | Dataset
- 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 | Dataset
- 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 | Papier
- 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

Raisonnement

Chain-of-Thought Hub 2023-5 | All | EN | CI | Paper | Github
- Publisher: University of Edinburgh et al.
- Taille: -
- 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 | Tout | 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 web
- 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 | Dataset
- 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 web
- 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

Connaissance

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 web
- 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 web
- 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 | Ensemble de données
- 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 web
- Publisher: LMSYS
- Taille: -
- 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 | Tout | 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 web
- 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.
- Taille: -
- 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
- Taille: -
- 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.
- Taille: -
- 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

Outil

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 web
- 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 | Tout | EN | HG | Paper | Github | Ensembles de données
- 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

Agent

AgentBench 2023-8 | All | EN | HG & CI & MC | Paper | Github | Site web
- 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
- Taille: -
- 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

Code

BIRD 2023-5 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | Site web
- 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 web
- 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 | Dataset
- 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 | Tout | 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 web
- 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.
- Taille: -
- 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

Loi

LAiW 2023-10 | Partial | ZH | CI | Paper | Github
- Publisher: Sichuan University et al.
- Taille: -
- 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 | Ensemble de données
- Publisher: Nanjing University et al.
- Taille: -
- 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 web
- 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/-

Médical

CBLUE 2022-5 | Tout | 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 web
- 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 | Ensemble de données
- 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.
- Taille: -
- 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

Financier

BBF-CFLEB 2023-2 | All | ZH | HG & CI | Paper | Github | Site web
- 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 web
- 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 web
- 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 web
- 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 web
- 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 web
- 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 | Dataset
- 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 | Dataset
- 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 | Papier
- 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 web
- Publisher: Toyota Technological Institute et al.
- Taille: -
- 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 web
- 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

Évaluation

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

Multitask

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.
- Taille: -
- 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 web
- 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 web
- 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 web
- Publisher: Stanford University et al.
- Taille: -
- 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

Multilingue

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 web
- Publisher: Carnegie Mellon University et al.
- Taille: -
- 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

Autre

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 web
- 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 web
- 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.
- Taille: -
- 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 web
- 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 | Ensemble de données
- 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 web
- Publisher: University of Pittsburgh et al.
- Train/Dev/Test/All Size: -/-/-/273.5K
- License: MIT
MultiRC 2018-6 | EN | Paper | Github | Dataset
- Publisher: University of Pennsylvania et al.
- Train/Dev/Test/All Size: -/-/-/9872
- License: MultiRC License
RACE 2017-4 | EN | Paper | Dataset | Site web
- Publisher: Carnegie Mellon University
- Train/Dev/Test/All Size: 87866/4887/4934/97687
- License: -
C3 2019-4 | ZH | Paper | Github | Site web
- Publisher: Cornell University et al.
- Train/Dev/Test/All Size: 11869/3816/3892/19577
- License: -
ReClor 2020-2 | EN | Paper | Site web
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
DREAM 2020-2 | EN | Paper | Github | Site web
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
QuAIL 2020-4 | EN | Paper | Site web
- 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 web
- 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 web
- 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 web
- 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 web
- Publisher: Johns Hopkins University et al.
- Train/Dev/Test/All Size: 100730/10000/10000/120730
- License: -
QuAC 2018-8 | EN | Paper | Dataset | Site web
- 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 web
- 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 | Dataset
- 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 web
- 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 web
- Publisher: Stanford University
- Train/Dev/Test/All Size: -/-/-/127K
- License: -
QASPER 2021-5 | EN | Paper | Site web
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: -/-/-/5049
- License: CC-BY-4.0
DuoRC 2018-7 | EN | Paper | Dataset | Site web
- 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 web
- Publisher: AI2
- Train/Dev/Test/All Size: 3370/869/3548/7787
- License: CC-BY-SA
CommonsenseQA 2018-11 | EN | Paper | Github | Dataset | Site web
- 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 web
- 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 web
- Publisher: Universidade da Coruna
- Train/Dev/Test/All Size: 2657/1366/2742/13530
- License: MIT
SciQ 2017-9 | EN | Paper | Dataset | Site web
- 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 web
- 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 web
- Publisher: Tel Aviv University et al.
- Train/Dev/Test/All Size: 2290/-/490/2780
- License: MIT
COPA 2011-6 | EN | Paper | Site web
- 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 | Dataset
- 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 web
- Publisher: AI2
- Train/Dev/Test/All Size: 2696/384/784/3864
- License: CC-BY-4.0
WIQA 2019-9 | EN | Paper | Dataset | Site web
- Publisher: AI2
- Train/Dev/Test/All Size: 29808/6894/3993/40695
- License: -
QASC 2019-10 | EN | Paper | Dataset | Site web
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: 8134/926/920/9980
- License: CC-BY-4.0
QuaRel 2018-11 | EN | Paper | Site web
- Publisher: AI2
- Train/Dev/Test/All Size: 1941/278/552/2771
- License: CC-BY-4.0
ROPES 2019-8 | EN | Paper | Dataset | Site web
- 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 | Ensemble de données
- 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 web
- 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 | Dataset
- 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: -

Mathématiques

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 web
- 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 web
- 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: -

Analyse des sentiments

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 | Papier
- 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 web
- Publisher: Google Research et al.
- Train/Dev/Test/All Size: 5749/1500/1379/8628
- License: -
AFQMC 2020-12 | ZH | Papier
- 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 | Papier
- 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 web
- 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 web
- 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 | Dataset
- 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 web
- 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 | Dataset
- 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 web
- 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 | Papier
- 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 web
- Publisher: Tsinghua University
- Train/Dev/Test/All Size: -/-/-/1672165
- License: MIT
CSLDCP 2021-7 | ZH | Paper | Github | Site web
- 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 web
- 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 web
- Publisher: Westlake University
- Train/Dev/Test/All Size: -/-/-/10181
- License: CC-BY-SA-4.0
Spider 2018-9 | EN & PL | Paper | Github | Site web
- 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 web
- 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 web
- 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 | Dataset
- 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 web
- 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 web
- 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 web
- Publisher: Tsinghua University
- Train/Dev/Test/All Size: -/-/-/70000
- License: CC-BY-SA-4.0
- Number of Relationship Categories: 100

Multitask

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

Remote Sensing

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

Général

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

Sujet

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

Multitask

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

Médical

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

Contact

Coordonnées:

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!

Citation

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}
}

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