Awesome LLMs Datasets 다운로드 - Awesome LLMs Datasets 소스 코드 다운로드

굉장한 선수-다타타 세트

5 가지 차원에서 기존 대표 LLMS 텍스트 데이터 세트를 요약하십시오 : 사전 훈련 Corpora, 미세 조정 명령 데이터 세트, 선호도 데이터 세트, 평가 데이터 세트 및 기존 NLP 데이터 세트 . (정기 업데이트)
새로운 데이터 세트 섹션이 추가되었습니다 : 다중 모달 대형 언어 모델 (MLLMS) 데이터 세트, 검색 증강 생성 (RAG) 데이터 세트 . (점진적 업데이트)

종이

논문 "대형 언어 모델을위한 데이터 세트 : 포괄적 인 설문 조사"가 발표되었습니다. (2024/2)

추상적인:

이 논문은 LLM (Langual Language Model) 데이터 세트에 대한 탐색을 시작하며, 이는 LLM의 놀라운 발전에 중요한 역할을합니다. 데이터 세트는 LLM의 개발을 유지하고 육성하는 루트 시스템과 유사한 기초 인프라 역할을합니다. 결과적으로, 이러한 데이터 세트를 조사하는 것은 연구에서 중요한 주제로 나타납니다. 현재 LLM 데이터 세트의 포괄적 인 개요 및 철저한 분석의 부족을 해결하고 현재 상태 및 향후 트렌드에 대한 통찰력을 얻기 위해이 설문 조사는 LLM 데이터 세트의 기본 측면을 5 가지 관점에서 통합하고 분류합니다. 훈련 공동; (2) 명령 미세 조정 데이터 세트; (3) 선호도 데이터 세트; (4) 평가 데이터 세트; (5) 전통적인 자연어 처리 (NLP) 데이터 세트. 설문 조사는 일반적인 도전에 대해 밝히고 향후 조사를위한 잠재적 인 길을 지적합니다. 또한 444 개의 데이터 세트의 통계, 8 개의 언어 범주 및 32 개의 도메인에 걸쳐있는 기존 사용 가능한 데이터 세트 리소스에 대한 포괄적 인 검토도 제공됩니다. 20 차원의 정보는 데이터 세트 통계에 통합됩니다. 설문 조사 대상 총 데이터 크기는 사전 훈련 Corpora의 경우 774.5 TB를 능가하고 다른 데이터 세트의 경우 700m 인스턴스를 능가합니다. 우리는 LLM 텍스트 데이터 세트의 전체 환경을 제시 하여이 분야의 연구원들을위한 포괄적 인 참조로 작용하고 향후 연구에 기여하는 것을 목표로합니다.

그림 1. 설문 조사의 전체 아키텍처. 더 나은 시야를 확대하십시오

데이터 세트 정보 모듈

다음은 데이터 세트 정보 모듈의 요약입니다.

코퍼스/데이터 세트 이름
발행자
출시 시간
- "X"는 알려지지 않은 달을 나타냅니다.
크기
공개적이든 아니든
- "모두"는 전체 오픈 소스를 나타냅니다.
- "부분"은 부분적으로 오픈 소스를 나타냅니다.
- "NOT"은 오픈 소스가 아님을 나타냅니다.
특허
언어
- "en"은 영어를 나타냅니다.
- "Zh"는 중국어를 나타냅니다.
- "AR"은 아랍어를 나타냅니다.
- "es"는 스페인어를 나타냅니다.
- "Ru"는 러시아어를 나타냅니다.
- "de"는 독일어를 나타냅니다.
- "코"는 한국을 나타냅니다.
- "LT"는 리투아니아어를 나타냅니다.
- "FA"는 페르시아/파시를 나타냅니다.
- "PL"은 프로그래밍 언어를 나타냅니다.
- "멀티"는 다국어를 나타내고 괄호의 숫자는 포함 된 언어 수를 나타냅니다.
건축 방법
- "HG"는 인간이 생성 된 코퍼스/데이터 세트를 나타냅니다.
- "MC"는 모델 구성 코퍼스/데이터 세트를 나타냅니다.
- "CI"는 기존 코퍼스/데이터 세트의 수집 및 개선을 나타냅니다.
범주
원천
도메인
지시 범주
선호도 평가 방법
- "VO"는 투표를 나타냅니다.
- "그래서"는 정렬을 나타냅니다.
- "SC"는 점수를 나타냅니다.
- "-H"는 인간이 수행 한 것을 나타냅니다.
- "-m"은 모델에 의해 수행 된 것을 나타냅니다.
질문 유형
- "SQ"는 주관적인 질문을 나타냅니다.
- "OQ"는 객관적인 질문을 나타냅니다.
- "Multi"는 여러 질문 유형을 나타냅니다.
평가 방법
- "CE"는 코드 평가를 나타냅니다.
- "그"는 인간의 평가를 나타냅니다.
- "나"는 모델 평가를 나타냅니다.
집중하다
평가 카테고리/하위 범주의 수
평가 범주
엔티티 카테고리 수 (NER 작업)
관계 카테고리 수 (RE 작업)

changelog

(2024/01/17) 굉장한 -llms-datasets 데이터 세트 저장소를 만듭니다.
(2024/02/02) 일부 데이터 세트에 대한 정보를 수정합니다. Dolma를 추가하십시오 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | Multi-Category).
(2024/02/15) AYA 컬렉션 추가 (명령 미세 조정 데이터 세트 | 일반 지침 미세 조정 데이터 세트 | HG & CI & MC); AYA 데이터 세트 (명령 미세 조정 데이터 세트 | 일반 명령 미세 조정 데이터 세트 | HG).
(2024/02/22) OpenMathInstruct-1 추가 (명령 미세 조정 데이터 세트 | 도메인 별 명령 미세 조정 데이터 세트 | 수학); Finben (평가 데이터 세트 | 재무).
(2024/04/05)
- 새 데이터 세트 섹션 추가 : (1) 다중 모달 대형 언어 모델 (MLLMS) 데이터 세트; (2) 검색 증강 생성 (RAG) 데이터 세트 .
- MMRS-1M 추가 (MLLMS 데이터 세트 | 명령 미세 조정 데이터 세트); videochat2-it (mllms 데이터 세트 | 명령 미세 조정 데이터 세트); InstructDoc (MLLMS 데이터 세트 | 명령 미세 조정 데이터 세트); Allava-4V 데이터 (MLLMS 데이터 세트 | 명령 미세 조정 데이터 세트); MVBENCH (MLLMS 데이터 세트 | 평가 데이터 세트); Olympiadbench (MLLMS 데이터 세트 | 평가 데이터 세트); MMMU (MLLMS 데이터 세트 | 평가 데이터 세트).
- 단서 벤치 마크 시리즈 추가 (평가 데이터 세트 | 평가 플랫폼); OpenLlm 리더 보드 (평가 데이터 세트 | 평가 플랫폼); OpenCompass (평가 데이터 세트 | 평가 플랫폼); MTEB 리더 보드 (평가 데이터 세트 | 평가 플랫폼); C-MTEB 리더 보드 (평가 데이터 세트 | 평가 플랫폼).
- NAH (Belessin-in-A-Haystack) (평가 데이터 세트 | 긴 텍스트); Tooleyes (평가 데이터 세트 | 도구); uhgeval (평가 데이터 세트 | 사실); Clongeval (평가 데이터 세트 | 긴 텍스트).
- MathPile 추가 (사전 훈련 Corpora | 도메인 별 사전 훈련 Corpora | Math); Wanjuan-CC (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 웹 페이지).
- IEPILE 추가 (명령 미세 조정 데이터 세트 | 일반 명령 미세 조정 데이터 세트 | CI); instructie (명령 미세 조정 데이터 세트 | 일반 명령 미세 조정 데이터 세트 | hg).
- CRUD-RAG (RAG 데이터 세트) 추가; Wikieval (Rag DataSets); RGB (RAG 데이터 세트); rag-instruct-benchmark-tester (Rag DataSets); ARES (RAG 데이터 세트).
(2024/04/06)
- GPQA 추가 (평가 데이터 세트 | 주제); MGSM (평가 데이터 세트 | 다국어); Halueval-Wild (평가 데이터 세트 | 사실); CMATH (평가 데이터 세트 | 주제); Finemath (평가 데이터 세트 | 주제); 실시간 QA (평가 데이터 세트 | 사실); Wyweb (평가 데이터 세트 | 주제); 중국식 (평가 데이터 세트 | 사실); 카운팅 스타 (평가 데이터 세트 | 긴 텍스트).
- Slimpajama (사전 훈련 Corpora | 일반 사전 훈련 Corpora | Multi-Category); Massivetext (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 멀티 카테고리); Madlad-400 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 웹 페이지); Minerva (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 다중 범주); ccaligned (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 병렬 코퍼스); Wikimatrix (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 병렬 코퍼스); OpenWebMath (사전 훈련 Corpora | 도메인 별 사전 훈련 Corpora | Math).
- WebQuestions (기존 NLP 데이터 세트 | 질문 답변 | 지식 QA) 추가.
- Alce (Rag DataSets)를 추가하십시오.
- 알파핀 추가 (명령 미세 조정 데이터 세트 | 도메인 별 명령 미세 조정 데이터 세트 | 기타); CoIG-CQIA (명령 미세 조정 데이터 세트 | 일반 명령 미세 조정 데이터 세트 | HG & CI).
(2024/06/15)
- 단서 추가 (평가 데이터 세트 | 의료); CHC- 벤치 (평가 데이터 세트 | 일반); CIF-Bench (평가 데이터 세트 | 일반); ACLUE (평가 데이터 세트 | 주제); LESC (평가 데이터 세트 | NLU); alignbench (평가 데이터 세트 | 멀티 태스킹); sciknoweval (평가 데이터 세트 | 주제).
- MAP-CC 추가 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | Multi-Category); FineWeb (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 웹 페이지); CCI 2.0 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 웹 페이지).
- Wildchat (명령 미세 조정 데이터 세트 | MC)을 추가하십시오.
- OpenHermeSpreferences 추가 (기본 설정 데이터 세트 | 정렬); huozi_rlhf_data (기본 설정 데이터 세트 | 투표); Helpsteer (기본 설정 데이터 세트 | 점수); HELPSTEER2 (기본 설정 데이터 세트 | 점수).
- MMT-Bench 추가 (MLLMS 데이터 세트 | 평가 데이터 세트); Moscar (MLLMS 데이터 세트 | 사전 훈련 Corpora); MM-NIAH (MLLMS 데이터 세트 | 평가 데이터 세트).
- 크래그를 추가하십시오 (래그 데이터 세트).
(2024/08/29)
- 게임 벤치 추가 (평가 데이터 세트 | 추론); Halludial (평가 데이터 세트 | 사실); 와일드 벤치 (평가 데이터 세트 | 일반); DomainEval (평가 데이터 세트 | 코드); Sysbench (평가 데이터 세트 | 일반); Kobest (평가 데이터 세트 | NLU); sarcasmbench (평가 데이터 세트 | NLU); C ³ 벤치 (평가 데이터 세트 | 주제); TableBench (평가 데이터 세트 | 추론); arablegaleval (평가 데이터 세트 | 법률).
- 다기성 추가 (MLLMS 데이터 세트 | 평가 데이터 세트); Obelisc (MLLMS 데이터 세트 | 사전 훈련 Corpora); 멀티 메이트 (MLLMS 데이터 세트 | 평가 데이터 세트).
- DCLM을 추가하십시오 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | 웹 페이지).
- 리투아니아 -QA-V1 추가 (명령 미세 조정 데이터 세트 | CI & MC); 재사용 (명령 미세 조정 데이터 세트 | HG & CI & MC); Kollm-converations (명령 미세 조정 데이터 세트 | CI).
(2024/09/04)
- Longwriter-6k (명령 미세 조정 데이터 세트 | CI & MC)를 추가하십시오.
- Medtrinity-25m 추가 (MLLMS 데이터 세트 | 평가 데이터 세트); MMIU (MLLMS 데이터 세트 | 평가 데이터 세트).
- Expository-Prose-V1 (사전 훈련 Corpora | 일반 사전 훈련 Corpora | Multi-Category) 추가.
- DebateQa (평가 데이터 세트 | 지식) 추가; 바늘 벤치 (평가 데이터 세트 | 긴 텍스트); Arabicmmlu (평가 데이터 세트 | 주제); persianmmlu (평가 데이터 세트 | 주제); tmmlu+ (평가 데이터 세트 | 주제).
- RageVal 추가 (Rag DataSets); LFRQA (RAG 데이터 세트); Multihop-RAG (RAG 데이터 세트).
데이터 세트 정보를 CSV 형식으로 해제합니다.

사전 훈련 Corpora
- 일반 사전 훈련 코포라
  - 웹 페이지
  - 언어 텍스트
  - 서적
  - 학업 자료
  - 암호
  - 평행 코퍼스
  - 소셜 미디어
  - 백과 사전
  - 다중 범주
- 도메인 별 사전 훈련 Corpora
  - 재정적인
  - 의료
  - 수학
  - 다른
명령 미세 조정 데이터 세트
- 일반 지침 미세 조정 데이터 세트
  - 인간 생성 데이터 세트 (HG)
  - 모델 구성 데이터 세트 (MC)
  - 기존 데이터 세트 수집 및 개선 (CI)
  - HG & CI
  - HG & MC
  - CI & MC
  - HG & CI & MC
- 도메인 별 명령 미세 조정 데이터 세트
  - 의료
  - 암호
  - 합법적인
  - 수학
  - 교육
  - 다른
기본 설정 데이터 세트
- 선호도 평가 방법
  - 투표
  - 종류
  - 점수
  - 다른
평가 데이터 세트
- 일반적인
- 시험
- 주제
- NLU
- 추리
- 지식
- 긴 텍스트
- 도구
- 대리인
- 암호
- ood
- 법
- 의료
- 재정적인
- 사회적 규범
- 사실
- 평가
- 멀티 태스킹
- 다국어
- 다른
- 평가 플랫폼
전통적인 NLP 데이터 세트
- 질문 대답
  - 독해력
    - 선택 및 판단
    - 클로즈 테스트
    - 답변 추출
    - 무제한 QA
  - 지식 QA
  - 추론 QA
- 텍스트 수입을 인식합니다
- 수학
- 코퍼레이션 해상도
- 감정 분석
- 시맨틱 매칭
- 텍스트 생성
- 텍스트 번역
- 텍스트 요약
- 텍스트 분류
- 텍스트 품질 평가
- 텍스트-코드
- 지명 된 엔티티 인식
- 관계 추출
- 멀티 태스킹
멀티 모달 대형 언어 모델 (MLLMS) 데이터 세트
- 사전 훈련 Corpora
- 명령 미세 조정 데이터 세트
- 평가 데이터 세트
검색 증강 생성 (RAG) 데이터 세트

사전 훈련 Corpora

사전 훈련 Corpora는 LLM의 사전 훈련 과정에서 사용되는 대규모 텍스트 데이터 컬렉션입니다.

일반 사전 훈련 코포라

일반적인 사전 훈련 Corpora는 다양한 도메인 및 소스의 광범위한 텍스트로 구성된 대규모 데이터 세트입니다. 그들의 주요 특성은 텍스트 내용이 단일 도메인에 국한되지 않으므로 일반적인 기초 모델을 교육하는 데 더 적합하다는 것입니다. Corpora는 데이터 범주를 기반으로 분류됩니다.

데이터 세트 정보 형식 ：

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

웹 페이지

CC-Stories 2018-6 | | en | ci | 종이 | github | 데이터 세트
- 게시자 : Google Brain
- 크기 : 31GB
- 라이센스 : -
- 출처 : 일반적인 크롤링
CC100 2020-7 | 모두 | 멀티 (100) | ci | 종이 | 데이터 세트
- 게시자 : Facebook AI
- 크기 : 2.5 tb
- 라이센스 : 일반적인 크롤링 용어
- 출처 : 일반적인 크롤링
Cluecorpus2020 2020-3 | 모두 | ZH | ci | 종이 | 데이터 세트
- 출판사 : 단서 조직
- 크기 : 100GB
- 라이센스 : MIT
- 출처 : 일반적인 크롤링
일반 크롤링 2007-X | 모두 | 멀티 | hg | 웹 사이트
- 출판사 : 일반 크롤링
- 크기 : -
- 라이센스 : 일반적인 크롤링 용어
- 출처 : 웹 크롤러 데이터
Culturax 2023-9 | 모두 | 멀티 (167) | ci | 종이 | 데이터 세트
- 출판사 : 오레곤 대학교 et al.
- 크기 : 27 TB
- 라이센스 : MC4 & Oscar 라이센스
- 출처 : MC4, 오스카
C4 2019-10 | 모두 | en | ci | 종이 | 데이터 세트
- 게시자 : Google Research
- 크기 : 12.68 tb
- 라이센스 : ODC-BY & Common Crawl 이용 약관
- 출처 : 일반적인 크롤링
MC4 2021-6 | 모두 | 멀티 (108) | ci | 종이 | 데이터 세트
- 게시자 : Google Research
- 크기 : 251GB
- 라이센스 : ODC-BY & Common Crawl 이용 약관
- 출처 : 일반적인 크롤링
오스카 22.01 2022-1 | 모두 | 멀티 (151) | ci | 종이 | 데이터 세트 | 웹 사이트
- 출판사 : Inria
- 크기 : 8.41 tb
- 라이센스 : CC0
- 출처 : 일반적인 크롤링
RealNews 2019-5 | 모두 | en | ci | 종이 | github
- 출판사 : University of Washington et al.
- 크기 : 120GB
- 라이센스 : Apache-2.0
- 출처 : 일반적인 크롤링
Redpajama-v2 2023-10 | 모두 | 멀티 (5) | ci | github | 데이터 세트 | 웹 사이트
- 출판사 : 함께 컴퓨터
- 크기 : 30.4 T 토큰
- 라이센스 : 일반적인 크롤링 용어
- 출처 : 일반 크롤링, C4 등
정제 Web 2023-6 | 부분 | en | ci | 종이 | 데이터 세트
- 출판사 : Falcon LLM 팀
- 크기 : 5000GB
- 라이센스 : ODC-By-1.0
- 출처 : 일반적인 크롤링
Wudaocorpora-Text 2021-6 | 부분 | ZH | hg | 종이 | 데이터 세트
- 출판사 : Baai et al.
- 크기 : 200GB
- 라이센스 : MIT
- 출처 : 중국 웹 페이지
Wanjuan-CC 2024-2 | 부분 | en | hg | 종이 | 데이터 세트
- 출판사 : 상하이 인공물 지능 연구소
- 크기 : 1 T 토큰
- 라이센스 : CC-By-4.0
- 출처 : 일반적인 크롤링
Madlad-400 2023-9 | 모두 | 멀티 (419) | hg | 종이 | github | 데이터 세트
- 게시자 : Google Deepmind et al.
- 크기 : 2.8 T 토큰
- 라이센스 : ODL-BY
- 출처 : 일반적인 크롤링
FineWeb 2024-4 | 모두 | en | ci | 데이터 세트
- 출판사 : Huggingfacefw
- 크기 : 15 TB 토큰
- 라이센스 : ODC-By-1.0
- 출처 : 일반적인 크롤링
CCI 2.0 2024-4 | 모두 | ZH | hg | DataSet1 | DataSet2
- 출판사 : Baai
- 크기 : 501GB
- 라이센스 : CCI 사용 범위
- 출처 : 중국 웹 페이지
DCLM 2024-6 | 모두 | en | ci | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : University of Washington et al.
- 크기 : 279.6 tb
- 라이센스 : 일반적인 크롤링 용어
- 출처 : 일반적인 크롤링

언어 텍스트

ANC 2003-X | 모두 | en | hg | 웹 사이트
- 출판사 : 미국 국립 과학 재단 등.
- 크기 : -
- 라이센스 : -
- 출처 : 미국 영어 텍스트
BNC 1994-X | 모두 | en | hg | 웹 사이트
- 출판사 : Oxford University Press et al.
- 크기 : 4124 텍스트
- 라이센스 : -
- 출처 : 영국 영어 텍스트
뉴스 크롤링 2019-1 | 모두 | 멀티 (59) | hg | 데이터 세트
- 출판사 : Ukri et al.
- 크기 : 110GB
- 라이센스 : CC0
- 출처 : 신문

서적

Anna 's Archive 2023-X | 모두 | 멀티 | hg | 웹 사이트
- 출판사 : Anna
- 크기 : 586.3 tb
- 라이센스 : -
- 출처 : SCI-HUB, Library Genesis, Z-Library 등
BookCorPusopen 2021-5 | 모두 | en | ci | 종이 | github | 데이터 세트
- 출판사 : Jack Bandy et al.
- 크기 : 17,868 권
- 라이센스 : Smashwords 서비스 약관
- 출처 : 토론토 북 코퍼스
PG-19 2019-11 | 모두 | en | hg | 종이 | github | 데이터 세트
- 출판사 : Deepmind
- 크기 : 11.74GB
- 라이센스 : Apache-2.0
- 출처 : Project Gutenberg
프로젝트 Gutenberg 1971-X | 모두 | 멀티 | hg | 웹 사이트
- 출판사 : Ibiblio et al.
- 크기 : -
- 라이센스 : 프로젝트 Gutenberg
- 출처 : 전자 책 데이터
Smashwords 2008-X | 모두 | 멀티 | hg | 웹 사이트
- 출판사 : Draft2Digital et al.
- 크기 : -
- 라이센스 : Smashwords 서비스 약관
- 출처 : 전자 책 데이터
토론토 북 코퍼스 2015-6 | | en | hg | 종이 | 웹 사이트
- 출판사 : Toronto University et al.
- 크기 : 11,038 권
- 라이센스 : MIT & SmashWords 서비스 약관
- 출처 : Smashwords

학업 자료

ARXIV 1991-X | 모두 | en | hg | 웹 사이트
- 출판사 : Paul Ginsparg et al.
- 크기 : -
- 라이센스 : ARXIV API의 이용 약관
- 출처 : arxiv preprint
S2ORC 2020-6 | 모두 | en | ci | 종이 | github
- 게시자 : AI2 et al.
- 크기 : 81.1 MB
- 라이센스 : ODC-By-1.0
- 출처 : Mag, Arxiv, PubMed 등

암호

BigQuery 2022-3 | | pl | ci | 종이 | github
- 게시자 : Salesforce Research
- 크기 : 341.1 GB
- 라이센스 : Apache-2.0
- 출처 : BigQuery
Github 2008-4 | 모두 | pl | hg | 웹 사이트
- 게시자 : Microsoft
- 크기 : -
- 라이센스 : -
- 출처 : 다양한 코드 프로젝트
PHI-1 2023-6 | | en & pl | HG & MC | 종이 | 데이터 세트
- 출판사 : Microsoft Research
- 크기 : 7 B 토큰
- 라이센스 : CC-By-NC-SA-3.0
- 출처 : 스택, Stackoverflow, GPT-3.5 Generation
스택 2022-11 | 모두 | pl (358) | hg | 종이 | 데이터 세트
- 출판사 : Servicenow Research et al.
- 크기 : 6 TB
- 라이센스 : 원래 라이센스의 조건
- 출처 : 허용 된 소스 코드 파일

평행 코퍼스

MTP 2023-9 | 모두 | en & zh | hg & ci | 데이터 세트
- 출판사 : Baai
- 크기 : 1.3 tb
- 라이센스 : BAAI 데이터 사용 프로토콜
- 출처 : 웹에서 중국어-영어 평행 텍스트 쌍
Multiun 2010-5 | 모두 | 멀티 (7) | hg | 종이 | 웹 사이트
- 출판사 : 독일 인공 지능 연구 센터 (DFKI) GMBH
- 크기 : 4353 MB
- 라이센스 : -
- 출처 : 유엔 문서
Paracrawl 2020-7 | 모두 | 멀티 (42) | hg | 종이 | 웹 사이트
- 출판사 : PrompSit et al.
- 크기 : 59996 파일
- 라이센스 : CC0
- 출처 : 웹 크롤러 데이터
Uncorpus v1.0 2016-5 | 모두 | 멀티 (6) | hg | 종이 | 웹 사이트
- 출판사 : 유엔 et al.
- 크기 : 799276 파일
- 라이센스 : -
- 출처 : 유엔 문서
ccaligned 2020-11 | 모두 | 멀티 (138) | hg | 종이 | 데이터 세트
- 게시자 : Facebook AI et al.
- 크기 : 392 m URL 쌍
- 라이센스 : -
- 출처 : 일반적인 크롤링
Wikimatrix 2021-4 | 모두 | 멀티 (85) | hg | 종이 | github | 데이터 세트
- 게시자 : Facebook AI et al.
- 크기 : 134 m 평행 문장
- 라이센스 : CC-By-Sa
- 출처 : Wikipedia

소셜 미디어

OpenWebText 2019-4 | 모두 | en | hg | 웹 사이트
- 출판사 : Brown University
- 크기 : 38GB
- 라이센스 : CC0
- 출처 : Reddit
푸시 시프트 레딧 2020-1 | 모두 | en | ci | 종이 | 웹 사이트
- 게시자 : Pushshift.io et al.
- 크기 : 2 tb
- 라이센스 : -
- 출처 : Reddit
Reddit 2005-6 | 모두 | en | hg | 웹 사이트
- 출판사 : Condé Nast Digital et al.
- 크기 : -
- 라이센스 : -
- 출처 : 소셜 미디어 게시물
Stackexchange 2008-9 | 모두 | en | hg | 데이터 세트 | 웹 사이트
- 게시자 : 스택 교환
- 크기 : -
- 라이센스 : CC-By-SA-4.0
- 출처 : 커뮤니티 질문 및 답변 데이터
WebText 2019-2 | 부분 | en | hg | 종이 | github | 데이터 세트
- 출판사 : Openai
- 크기 : 40GB
- 라이센스 : MIT
- 출처 : Reddit
Zhihu 2011-1 | 모두 | ZH | hg | 웹 사이트
- 출판사 : Beijing Zhizhe Tianxia Technology Co., Ltd
- 크기 : -
- 라이센스 : Zhihu 사용자 계약
- 출처 : 소셜 미디어 게시물

백과 사전

Baidu Baike 2008-4 | 모두 | ZH | hg | 웹 사이트
- 출판사 : 바이두
- 크기 : -
- 라이센스 : Baidu Baike 사용자 계약
- 출처 : 백과 사전 컨텐츠 데이터
Tigerbot-Wiki 2023-5 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 205MB
- 라이센스 : Apache-2.0
- 출처 : Baidu Baike
Wikipedia 2001-1 | 모두 | 멀티 | hg | 데이터 세트 | 웹 사이트
- 출판사 : Wikimedia Foundation
- 크기 : -
- 라이센스 : CC-BY-SA-3.0 & GFDL
- 출처 : 백과 사전 컨텐츠 데이터

다중 범주

아랍어 텍스트 2022 2022-12 | 모두 | ar | hg & ci | 데이터 세트
- 출판사 : Baai et al.
- 크기 : 201.9GB
- 라이센스 : CC-By-SA-4.0
- 출처 : 아라비아 브, 오스카, CC100 등
MNBVC 2023-1 | 모두 | ZH | hg & ci | github | 데이터 세트
- 출판사 : Liwu 커뮤니티
- 크기 : 20811 GB
- 라이센스 : MIT
- 출처 : 중국 책, 웹 페이지, 논문 등
Redpajama-v1 2023-4 | 모두 | 멀티 | hg & ci | github | 데이터 세트
- 출판사 : 함께 컴퓨터
- 크기 : 1.2 T 토큰
- 라이센스 : -
- 출처 : 일반 크롤링, Github, 서적 등
뿌리 2023-3 | 부분 | 멀티 (59) | hg & ci | 종이 | 데이터 세트
- 출판사 : Hugging Face et al.
- 크기 : 1.61 tb
- 라이센스 : Bloom Open-Rail-M
- 출처 : Oscar, Github 등
더미 2021-1 | 모두 | en | hg & ci | 종이 | github | 데이터 세트
- 출판사 : Eleutheai
- 크기 : 825.18 GB
- 라이센스 : MIT
- 출처 : 책, Arxiv, Github 등
tigerbot_pretrain_en 2023-5 | 부분 | en | ci | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 51GB
- 라이센스 : Apache-2.0
- 출처 : 영어 서적, 웹 페이지, en-wiki 등
tigerbot_pretrain_zh 2023-5 | 부분 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 55GB
- 라이센스 : Apache-2.0
- 출처 : 중국 책, 웹 페이지, Zh-Wiki 등
Wanjuantext-1.0 2023-8 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : 상하이 AI 실험실
- 크기 : 1094 GB
- 라이센스 : CC-By-4.0
- 출처 : 웹 페이지, 백과 사전, 서적 등
돌마 2024-1 | 모두 | en | hg & ci | 종이 | github | 데이터 세트
- 게시자 : AI2 et al.
- 크기 : 11519 GB
- 라이센스 : MR 계약
- 출처 : Project Gutenberg, C4, Reddit 등
Slimpajama 2023-6 | 모두 | en | hg & ci | github | 데이터 세트 | 웹 사이트
- 출판사 : Cerebras et al.
- 크기 : 627 B 토큰
- 라이센스 : -
- 출처 : Common Crawl, C4, Github 등
Massivetext 2021-12 | | 멀티 | hg & ci | 종이
- 게시자 : Google DeepMind
- 크기 : 10.5 tb
- 라이센스 : -
- 출처 : MassiveWeb, C4, Books 등
미네르바 2022-6 | | en | hg | 종이
- 게시자 : Google Research
- 크기 : 38.5 B 토큰
- 라이센스 : -
- 출처 : Arxiv, 웹 페이지 등
MAP-CC 2024-4 | 모두 | ZH | hg | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : 멀티 모달 아트 프로젝션 연구 커뮤니티 et al.
- 크기 : 840.48 B 토큰
- 라이센스 : CC-By-NC-ND-4.0
- 출처 : 중국 공통 크롤링, 중국 백과 사전, 중국 책 등
Expository-Prose-V1 2024-8 | 모두 | en | hg & ci | 종이 | github | 데이터 세트
- 출판사 : Pints.ai Labs
- 크기 : 56 B 토큰
- 라이센스 : MIT
- 출처 : Arxiv, Wikipedia, Gutenberg 등

도메인 별 사전 훈련 Corpora

도메인 별 사전 훈련 Corpora는 특정 필드 또는 주제에 맞게 사용자 정의 된 LLM 데이터 세트입니다. 코퍼스의 유형은 일반적으로 LLM의 증분 사전 훈련 단계에서 사용됩니다. Corpora는 데이터 도메인을 기반으로 분류됩니다.

데이터 세트 정보 형식 ：

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

재정적인

BBT-Fincorpus 2023-2 | 부분 | ZH | hg | 종이 | github | 웹 사이트
- 출판사 : Fudan University et al.
- 크기 : 256GB
- 라이센스 : -
- 출처 : 회사 발표, 연구 보고서, 금융
- 카테고리 : 멀티
- 도메인 : 금융
Fincorpus 2023-9 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Du Xiaoman
- 크기 : 60.36 GB
- 라이센스 : Apache-2.0
- 출처 : 회사 발표, 재무 뉴스, 재무 시험 질문
- 카테고리 : 멀티
- 도메인 : 금융
Finglm 2023-7 | 모두 | ZH | hg | github
- 출판사 : 지식 Atlas et al.
- 크기 : 69GB
- 라이센스 : Apache-2.0
- 출처 : 상장 회사의 연례 보고서
- 카테고리 : 언어 텍스트
- 도메인 : 금융
Tigerbot-earning 2023-5 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 488MB
- 라이센스 : Apache-2.0
- 출처 : 재무 보고서
- 카테고리 : 언어 텍스트
- 도메인 : 금융
Tigerbot-Research 2023-5 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 696MB
- 라이센스 : Apache-2.0
- 출처 : 연구 보고서
- 카테고리 : 언어 텍스트
- 도메인 : 금융

의료

Medical-PT 2023-5 | 모두 | ZH | ci | github | 데이터 세트
- 출판사 : Ming Xu
- 크기 : 632.78 MB
- 라이센스 : Apache-2.0
- 출처 : 의료 백과 사전 데이터, 의료 교과서
- 카테고리 : 멀티
- 도메인 : 의료
PubMed Central 2000-2 | 모두 | en | hg | 웹 사이트
- 출판사 : NCBI
- 크기 : -
- 라이센스 : PMC 저작권 통지
- 출처 : 생물 의학 과학 문헌
- 카테고리 : 학업 자료
- 도메인 : 의료

수학

증명 -22023-10 | 모두 | en | hg & ci | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : Princeton University et al.
- 크기 : 55 B 토큰
- 라이센스 : -
- 출처 : Arxiv, OpenWebMath, AlgebraicStack
- 카테고리 : 멀티
- 도메인 : 수학
Mathpile 2023-12 | 모두 | en | hg | 종이 | github | 데이터 세트
- 출판사 : Shanghai Jiao Tong University et al.
- 크기 : 9.5 B 토큰
- 라이센스 : CC-By-NC-SA-4.0
- 출처 : 교과서, Wikipedia, Proofwiki, CommonCrawl, Stackexchange, Arxiv
- 카테고리 : 멀티
- 도메인 : 수학
OpenWebMath 2023-10 | 모두 | en | hg | 종이 | github | 데이터 세트
- 출판사 : Toronto University et al.
- 크기 : 14.7 B 토큰
- 라이센스 : ODC-By-1.0
- 출처 : 일반적인 크롤링
- 카테고리 : 웹 페이지
- 도메인 : 수학

다른

Tigerbot-Law 2023-5 | 모두 | ZH | hg | 종이 | github | 데이터 세트
- 출판사 : Tigerbot
- 크기 : 29.9MB
- 라이센스 : Apache-2.0
- 출처 : 법적 조항
- 카테고리 : 언어 텍스트
- 도메인 : 법률
Transgpt-PT 2023-7 | 모두 | ZH | hg | github | 데이터 세트
- 출판사 : 베이징 Jiaotong University
- 크기 : 35.8MB
- 라이센스 : Apache-2.0
- 출처 : 기술 문서, 엔지니어링 건설 정보, 통계 데이터 등
- 카테고리 : 멀티
- 도메인 : 운송

명령 미세 조정 데이터 세트

명령 미세 조정 데이터 세트는 "명령 입력"및 "답변 출력"을 포함하는 일련의 텍스트 쌍으로 구성됩니다. "명령 입력"은 인간이 모델에 대한 요청을 나타냅니다. 분류, 요약, 역설 등과 같은 다양한 유형의 지침이 있습니다.“답변 출력”은 지시에 따라 모델에 의해 생성 된 응답이며 인간의 기대와 일치합니다.

일반 지침 미세 조정 데이터 세트

일반 명령 미세 조정 데이터 세트에는 도메인 제한이없는 하나 이상의 명령 범주가 포함되어 있으며, 주로 일반 작업에서 LLM의 명령어를 따르는 기능을 향상시키는 것을 목표로합니다. 데이터 세트는 시공 방법에 따라 분류됩니다.

데이터 세트 정보 형식 ：

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

인간 생성 데이터 세트 (HG)

Databricks-Dolly-15K 2023-4 | 모두 | en | hg | 데이터 세트 | 웹 사이트
- 출판사 : 데이터 사업
- 크기 : 15011 인스턴스
- 라이센스 : CC-By-SA-3.0
- 출처 : 다양한 명령 범주를 기반으로 수동으로 생성됩니다
- 교육 범주 : 멀티
습득 Wild_v2 2023-6 | 모두 | en & zh | hg | github
- 출판사 : 싱가포르 국립 대학교
- 크기 : 110k 인스턴스
- 라이센스 : -
- 출처 : 웹에서 수집
- 교육 범주 : 멀티
LCCC 2020-8 | 모두 | ZH | hg | 종이 | github
- 출판사 : Tsinghua University et al.
- 크기 : 12m 인스턴스
- 라이센스 : MIT
- 출처 : 소셜 미디어의 크롤링 사용자 상호 작용
- 교육 범주 : 멀티
OASST1 2023-4 | 모두 | 멀티 (35) | hg | 종이 | github | 데이터 세트
- 게시자 : OpenAssistant
- 크기 : 161443 인스턴스
- 라이센스 : Apache-2.0
- 출처 : 인간이 생성하고 주석을 달았습니다
- 교육 범주 : 멀티
OL-CC 2023-6 | 모두 | ZH | hg | 데이터 세트
- 출판사 : Baai
- 크기 : 11655 인스턴스
- 라이센스 : Apache-2.0
- 출처 : 인간이 생성하고 주석을 달았습니다
- 교육 범주 : 멀티
Zhihu-Kol 2023-3 | 모두 | ZH | hg | github | 데이터 세트
- 출판사 : Wangrui6
- 크기 : 1006218 인스턴스
- 라이센스 : MIT
- 출처 : Zhihu에서 크롤링
- 교육 범주 : 멀티
AYA 데이터 세트 2024-2 | 모두 | 멀티 (65) | hg | 종이 | 데이터 세트 | 웹 사이트
- 게시자 : AI Community et al.
- 크기 : 204K 인스턴스
- 라이센스 : Apache-2.0
- 출처 : AYA 주석 플랫폼을 통해 수동으로 수집 및 주석
- 교육 범주 : 멀티
강사 2023-5 | 모두 | en & zh | hg | 종이 | github | 데이터 세트
- 출판사 : Zhejiang University et al.
- 크기 : 371700 인스턴스
- 라이센스 : MIT
- 출처 : Baidu Baike, Wikipedia
- 지시 범주 : 추출

모델 구성 데이터 세트 (MC)

alpaca_data 2023-3 | 모두 | en | MC | github
- 출판사 : Stanford Alpaca
- 크기 : 52k 인스턴스
- 라이센스 : Apache-2.0
- 출처 : aplaca_data 프롬프트와 함께 Text-davinci-003에 의해 생성되었습니다
- 교육 범주 : 멀티
Belle_generated_chat 2023-5 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 396004 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Chatgpt에 의해 생성됩니다
- 교육 범주 : 생성
Belle_multiturn_chat 2023-5 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 831036 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Chatgpt에 의해 생성됩니다
- 교육 범주 : 멀티
Belle_train_0.5M_CN 2023-4 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 519255 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Text-Davinci-003에 의해 생성됩니다
- 교육 범주 : 멀티
belle_train_1m_cn 2023-4 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 917424 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Text-Davinci-003에 의해 생성됩니다
- 교육 범주 : 멀티
belle_train_2m_cn 2023-5 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 2m 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Chatgpt에 의해 생성됩니다
- 교육 범주 : 멀티
Belle_train_3.5m_cn 2023-5 | 모두 | ZH | MC | github | 데이터 세트
- 출판사 : 벨
- 크기 : 3606402 인스턴스
- 라이센스 : GPL-3.0
- 출처 : Chatgpt에 의해 생성됩니다
- 교육 범주 : 멀티
낙타 2023-3 | 모두 | 멀티 & pl | MC | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : Kaust
- 크기 : 1659328 인스턴스
- 라이센스 : CC-By-NC-4.0
- 출처 : 2 개의 GPT-3.5-Turbo 에이전트가 생성 한 대화
- 교육 범주 : 멀티
chatgpt_corpus 2023-6 | 모두 | ZH | MC | github
- 출판사 : Plexpt
- 크기 : 3270k 인스턴스
- 라이센스 : GPL-3.0
- 출처 : GPT-3.5-Turbo에 의해 생성되었습니다
- 교육 범주 : 멀티
습득 Wild_v1 2023-3 | 모두 | en & zh | MC | github
- 출판사 : 싱가포르 국립 대학교
- 크기 : 104K 인스턴스
- 라이센스 : -
- 출처 : OpenAI API에 의해 생성됩니다
- 교육 범주 : 멀티
LMSYS-Chat-1M 2023-9 | 모두 | 멀티 | MC | 종이 | 데이터 세트
- 출판사 : UC Berkeley et al.
- 크기 : 1m 인스턴스
- 라이센스 : LMSYS-Chat-1M 라이센스
- 출처 : 여러 LLM에 의해 생성됩니다
- 교육 범주 : 멀티
MOSS_002_SFT_DATA 2023-4 | 모두 | en & zh | MC | github | 데이터 세트
- 출판사 : Fudan University
- 크기 : 1161137 인스턴스
- 라이센스 : CC-By-NC-4.0
- 출처 : Text-Davinci-003에 의해 생성됩니다
- 교육 범주 : 멀티
MOSS_003_SFT_DATA 2023-4 | 모두 | en & zh | MC | github | 데이터 세트
- 출판사 : Fudan University
- 크기 : 1074551 인스턴스
- 라이센스 : CC-By-NC-4.0
- 출처 : Moss-002의 대화 데이터 및 GPT-3.5-Turbo에 의해 생성됩니다
- 교육 범주 : 멀티
Moss_003_SFT_PLUGIN_DATA 2023-4 | 부분 | en & zh | MC | github | 데이터 세트
- 출판사 : Fudan University
- 크기 : 300k 인스턴스
- 라이센스 : CC-By-NC-4.0
- 출처 : 플러그인 및 LLM에 의해 생성됩니다
- 교육 범주 : 멀티
OpenChat 2023-7 | 모두 | en | MC | 종이 | github | 데이터 세트
- 출판사 : Tsinghua University et al.
- 크기 : 70k 인스턴스
- 라이센스 : MIT
- 출처 : sharegpt
- 교육 범주 : 멀티
redgpt-dataset-v1-cn 2023-4 | 부분 | ZH | MC | github
- 출판사 : Da-Southampton
- 크기 : 50k 인스턴스
- 라이센스 : Apache-2.0
- 출처 : LLMS에 의해 생성됩니다
- 교육 범주 : 멀티
자체 조정 2022-12 | 모두 | en | MC | 종이 | github
- 출판사 : University of Washington et al.
- 크기 : 52445 인스턴스
- 라이센스 : Apache-2.0
- 출처 : GPT-3에 의해 생성됩니다
- 교육 범주 : 멀티
ShareChat 2023-4 | 모두 | 멀티 | MC | 웹 사이트
- 출판사 : ShareChat
- 크기 : 90k 인스턴스
- 라이센스 : CC0
- 출처 : sharegpt
- 교육 범주 : 멀티
sharegpt-chinese-english-90k 2023-7 | 모두 | en & zh | MC | github | 데이터 세트
- 출판사 : Shareai
- 크기 : 90k 인스턴스
- 라이센스 : Apache-2.0
- 출처 : sharegpt
- 교육 범주 : 멀티
sharegpt90k 2023-4 | 모두 | en | MC | 데이터 세트
- 출판사 : Ryokoai
- 크기 : 90k 인스턴스
- 라이센스 : CC0
- 출처 : sharegpt
- 교육 범주 : 멀티
Ultrachat 2023-5 | 모두 | en | MC | 종이 | github
- 출판사 : Tsinghua University
- 크기 : 1468352 인스턴스
- 라이센스 : CC-By-NC-4.0
- 출처 : 두 Chatgpt 에이전트가 생성 한 대화
- 교육 범주 : 멀티
부 자연스러운 지침 2022-12 | 모두 | en | MC | 종이 | github
- 출판사 : Tel Aviv University et al.
- 크기 : 240670 인스턴스
- 라이센스 : MIT
- 출처 : LLMS에 의해 생성됩니다
- 교육 범주 : 멀티
WebGLM-QA 2023-6 | 모두 | en | MC | 종이 | github | 데이터 세트
- 출판사 : Tsinghua University et al.
- 크기 : 44979 인스턴스
- 라이센스 : Apache-2.0
- 출처 : LLM 내 컨텍스트 부트 스트랩을 통해 WebGLM-QA를 구성합니다
- 교육 범주 : QA를 엽니 다
Wizard_evol_instruct_196k 2023-6 | 모두 | en | MC | 종이 | github | 데이터 세트
- 출판사 : Microsoft et al.
- 크기 : 196K 인스턴스
- 라이센스 : -
- 출처 : Evol-Instruct 방법을 통해 Evolve 지침
- 교육 범주 : 멀티
Wizard_evol_instruct_70k 2023-5 | 모두 | en | MC | 종이 | github | 데이터 세트
- 출판사 : Microsoft et al.
- 크기 : 70k 인스턴스
- 라이센스 : -
- 출처 : Evol-Instruct 방법을 통해 Evolve 지침
- 교육 범주 : 멀티
Wildchat 2024-5 | 부분 | 멀티 | MC | 종이 | 데이터 세트
- 출판사 : Cornell University et al.
- 크기 : 1039785 인스턴스
- 라이센스 : AI2 충격 라이센스
- 출처 : 사용자와 chatgpt 간의 대화, GPT-4
- 교육 범주 : 멀티

기존 데이터 세트 수집 및 개선 (CI)

CrossFit 2021-4 | 모두 | en | ci | 종이 | github
- 출판사 : 남부 캘리포니아 대학교
- 크기 : 269 데이터 세트
- 라이센스 : -
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
Dialogstudio 2023-7 | 모두 | en | ci | 종이 | github | 데이터 세트
- 게시자 : Salesforce AI et al.
- 크기 : 87 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
Dynosaur 2023-5 | 모두 | en | ci | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : Ucla et al.
- 크기 : 801900 인스턴스
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
Flan-Mini 2023-7 | 모두 | en | ci | 종이 | github | 데이터 세트
- 출판사 : 싱가포르 기술 및 디자인
- 크기 : 1.34m 인스턴스
- 라이센스 : CC
- 출처 : 다양한 명령 미세 조정 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
FLAN 2021 2021-9 | 모두 | 멀티 | ci | 종이 | github
- 게시자 : Google Research
- 크기 : 62 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
FLAN 2022 2023-1 | 부분 | 멀티 | ci | 종이 | github | 데이터 세트
- 게시자 : Google Research
- 크기 : 1836 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 명령 미세 조정 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
instructdial 2022-5 | 모두 | en | ci | 종이 | github
- 출판사 : Carnegie Mellon University
- 크기 : 59 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
자연 지침 2021-4 | 모두 | en | ci | 종이 | github | 데이터 세트
- 출판사 : Alen Institute for Ai et al.
- 크기 : 61 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
OIG 2023-3 | 모두 | en | ci | 데이터 세트
- 출판사 : Laion
- 크기 : 3878622 인스턴스
- 라이센스 : Apache-2.0
- 출처 : 다양한 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
개방형 플라 타투스 2023-8 | 모두 | en | ci | 종이 | github | 데이터 세트 | 웹 사이트
- 출판사 : 보스턴 대학교
- 크기 : 24926 인스턴스
- 라이센스 : -
- 출처 : 다양한 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
OPT-IML 벤치 2022-12 | | 멀티 | ci | 종이 | github
- 출판사 : 메타 AI
- 크기 : 2000 데이터 세트
- 라이센스 : MIT
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
프롬프트 소스 2022-2 | 모두 | en | ci | 종이 | github
- 출판사 : Brown University et al.
- 크기 : 176 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
초자연적 인 지침 2022-4 | 모두 | 멀티 | ci | 종이 | github
- 출판사 : Univ. 워싱턴 등
- 크기 : 1616 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
T0 2021-10 | 모두 | en | ci | 종이 | DataSet1 | DataSet2
- 출판사 : Hugging Face et al.
- 크기 : 62 데이터 세트
- 라이센스 : Apache-2.0
- 출처 : 다양한 NLP 데이터 세트의 수집 및 개선
- 교육 범주 : 멀티
Unifiedskg 2022-3 | 모두 | en | ci | 종이 | github
- 출판사 : 홍콩 대학교 et al.
- 크기 : 21 데이터 세트
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
xP3 2022-11 | 모두 | 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 | 모두 | EN & ZH | CI | Paper | github | 데이터 세트
- 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 | 모두 | KO | CI | 데이터 세트
- Publisher: davidkim205
- Size: 1122566 instances
- License: Apache-2.0
- Source: Collection and improvement of Korean datasets
- Instruction Category: Multi

HG & CI

Firefly 2023-4 | 모두 | ZH | HG & CI | github | 데이터 세트
- 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 | 모두 | EN | HG & CI | Paper | 데이터 세트
- 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 | 모두 | ZH | HG & CI | Paper | 데이터 세트
- 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 | 종이
- Publisher: OpenAI
- Size: 14378 instances
- License: -
- Source: Platform Q&A data and manual labeling
- Instruction Category: Multi

CI & MC

Alpaca_GPT4_data 2023-4 | 모두 | 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 | 모두 | ZH | CI & MC | github | 데이터 세트
- 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 | 모두 | Multi (52) | CI & MC | Paper | github | 데이터 세트
- 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 | 데이터 세트
- 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 | 모두 | EN | CI & MC | Paper | github | 데이터 세트
- 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 | 모두 | Multi | CI & MC | Dataset | 웹 사이트
- 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 | 모두 | EN | CI & MC | Paper | github | 데이터 세트
- 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 | 모두 | EN & ZH | CI & MC | Paper | github | 데이터 세트
- 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 | 모두 | EN | CI & MC | Paper | github | 데이터 세트
- 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 | 모두 | EN & ZH | CI & MC | github | 데이터 세트
- 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 | 모두 | Multi | CI & MC | Paper | 데이터 세트
- 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 | 모두 | ZH | CI & MC | github | 데이터 세트
- 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 | 모두 | LT | CI & MC | Paper | 데이터 세트
- 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 | 모두 | EN & ZH | CI & MC | Paper | github | 데이터 세트
- 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 | 모두 | ZH | HG & CI & MC | Paper | github | 데이터 세트
- Publisher: BAAI
- Size: 191191 instances
- License: Apache-2.0
- Source: Translated instructions, Leetcode, Chinese exams, etc.
- Instruction Category: Multi
HC3 2023-1 | 모두 | 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 | 모두 | Multi | HG & CI & MC | Paper | github | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 모두 | Multi (114) | HG & CI & MC | Paper | Dataset | 웹 사이트
- 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:

의료

ChatDoctor 2023-3 | 모두 | EN | HG & MC | Paper | github | 데이터 세트
- 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 | 모두 | ZH | MC | github | 데이터 세트
- 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 | 모두 | ZH | HG | Paper | github | 데이터 세트
- Publisher: Zhengzhou University
- Size: 68023 instances
- License: MIT
- Source: Real conversations between doctors and patients
- Instruction Category: Multi
- Domain: Medical
DISC-Med-SFT 2023-8 | All | ZH | HG & CI | Paper | github | Dataset | 웹 사이트
- 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 | 모두 | ZH | HG & MC | Paper | github | 데이터 세트
- Publisher: The Chinese University of Hong Kong et al.
- Size: 226042 instances
- License: Apache-2.0
- Source: Real conversations between doctors and patients & Generated by ChatGPT
- Instruction Category: Multi
- Domain: Medical
Huatuo-26M 2023-5 | Partial | ZH | CI | Paper | github
- Publisher: The Chinese University of Hong Kong et al.
- Size: 26504088 instances
- License: Apache-2.0
- Source: Collection and improvement of various datasets
- Instruction Category: Multi
- Domain: Medical
MedDialog 2020-4 | All | EN & ZH | HG | Paper | github
- Publisher: UC San Diego
- Size: 3.66M instances
- License: -
- Source: Real conversations between doctors and patients
- Instruction Category: Multi
- Domain: Medical
Medical Meadow 2023-4 | All | EN | HG & CI | Paper | github | 데이터 세트
- Publisher: University Hospital Aachen et al.
- Size: 160076 instances
- License: GPL-3.0
- Source: Crawl data from the Internet & Collection and improvement of various NLP datasets
- Instruction Category: Multi
- Domain: Medical
Medical-sft 2023-5 | All | EN & ZH | CI | github | 데이터 세트
- 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 | 데이터 세트
- Publisher: Zhejiang University
- Size: 20K instances
- License: GPL-3.0
- Source: Collection and improvement of various datasets
- Instruction Category: Multi
- Domain: Medical
ShenNong_TCM_Dataset 2023-6 | All | ZH | MC | github | 데이터 세트
- Publisher: michael-wzhu
- Size: 112565 instances
- License: Apache-2.0
- Source: Generated by ChatGPT
- Instruction Category: Multi
- Domain: Medical

암호

Code_Alpaca_20K 2023-3 | All | EN & PL | MC | github | 데이터 세트
- Publisher: Sahil Chaudhary
- Size: 20K instances
- License: Apache-2.0
- Source: Generated by Text-Davinci-003
- Instruction Category: Code
- Domain: Code
CodeContest 2022-3 | All | EN & PL | CI | Paper | github
- Publisher: DeepMind
- Size: 13610 instances
- License: Apache-2.0
- Source: Collection and improvement of various datasets
- Instruction Category: Code
- Domain: Code
CommitPackFT 2023-8 | All | EN & PL (277) | HG | Paper | github | 데이터 세트
- Publisher: Bigcode
- Size: 702062 instances
- License: MIT
- Source: GitHub Action dump
- Instruction Category: Code
- Domain: Code
ToolAlpaca 2023-6 | All | EN & PL | HG & MC | Paper | github
- Publisher: Chinese Information Processing Laboratory et al.
- Size: 3928 instances
- License: Apache-2.0
- Source: Manually filter APIs & Generated by ChatGPT
- Instruction Category: Code
- Domain: Code
ToolBench 2023-7 | All | EN & PL | HG & MC | Paper | github
- Publisher: Tsinghua University et al.
- Size: 126486 instances
- License: Apache-2.0
- Source: Manually filter APIs & Generated by ChatGPT
- Instruction Category: Code
- Domain: Code

합법적인

DISC-Law-SFT 2023-9 | Partial | ZH | HG & CI & MC | Paper | github | 웹 사이트
- Publisher: Fudan University et al.
- Size: 403K instances
- License: Apache-2.0
- Source: Open source datasets & Legal-related Text Content & Generated by GPT-3.5-Turbo
- Instruction Category: Multi
- Domain: Law
HanFei 1.0 2023-5 | All | ZH | - | github | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- Publisher: Peking Universit
- Size: 21476 instances
- License: Apache-2.0
- Source: Generated by ChatGPT with other datasets' prompts
- Instruction Category: Multi
- Domain: Law

수학

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

교육

Child_chat_data 2023-8 | All | ZH | HG & MC | github
- Publisher: Harbin Institute of Technology et al.
- Size: 5000 instances
- License: -
- Source: Real conversations & Generated by GPT-3.5-Turbo
- Instruction Category: Multi
- Domain: Education
Educhat-sft-002-data-osm 2023-7 | All | EN & ZH | CI | Paper | github | 데이터 세트
- Publisher: East China Normal University et al.
- Size: 4279419 instances
- License: CC-BY-NC-4.0
- Source: Collection and improvement of various datasets
- Instruction Category: Multi
- Domain: Education
TaoLi_data 2023-X | All | ZH | HG & CI | github | 데이터 세트
- 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

다른

DISC-Fin-SFT 2023-10 | Partial | ZH | HG & CI & MC | Paper | github | 웹 사이트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- Publisher: Allenai
- Size: 165681 instances
- License: CC-BY-4.0
- Source: Generated by humans with GPT-3 created prompts
- Instruction Category: Social Norms
- Domain: Social Norms
TransGPT-sft 2023-7 | All | ZH | HG | github | 데이터 세트
- 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:

투표

Chatbot_arena_conversations 2023-6 | All | Multi | HG & MC | Paper | 데이터 세트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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

종류

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

점수

Stack-Exchange-Preferences 2021-12 | All | EN | HG | Paper | 데이터 세트
- 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 | 데이터 세트
- Publisher: OpenAI
- Size: 19578 instances
- License: -
- Domain: General
- Instruction Category: Multi
- Preference Evaluation Method: SC-H
- Source: Open source datasets & Manual scoring
Alpaca_comparison_data 2023-3 | All | EN | MC | github
- Publisher: Stanford Alpaca
- Size: 51K instances
- License: Apache-2.0
- Domain: General
- Instruction Category: Multi
- Preference Evaluation Method: SC-M
- Source: Generated by three LLMs & GPT-4 scoring
Stable_Alignment 2023-5 | All | EN | MC | Paper | github
- Publisher: Dartmouth College et al.
- Size: 169K instances
- License: Apache-2.0
- Domain: General
- Instruction Category: Multi
- Preference Evaluation Method: SC-M
- Source: Generated by LLMs & Model scoring
UltraFeedback 2023-10 | All | EN | CI & MC | Paper | github | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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

다른

Medical-rlhf 2023-5 | All | ZH | CI & MC | github | 데이터 세트
- 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:

일반적인

AlpacaEval 2023-5 | All | EN | CI & MC | Paper | github | Dataset | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Allen Institute for AI et al.
- Size: 1024 instances
- License: AI2 ImpACT License
- Question Type: SQ
- Evaluation Method: ME
- Focus: An automated evaluation framework designed to benchmark LLMs using challenging, real-world user queries.
- Numbers of Evaluation Categories/Subcategories: 11/-
- Evaluation Category: Information seeking, Coding & Debugging, Creative writing, Reasoning, Planning, Math, Editing, Data analysis, Role playing, Brainstorming, Advice seeking
SysBench 2024-8 | All | EN | HG | Paper | github | 데이터 세트
- 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

시험

AGIEval 2023-4 | All | EN & ZH | HG & CI | Paper | github | 데이터 세트
- 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

주제

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 | 웹 사이트
- Publisher: Tianjin University
- Size: -
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- Publisher: University of California et al.
- Size: 21208 instances
- License: CC-BY-NC-SA-4.0
- Question Type: OQ
- Evaluation Method: CE
- Focus: Science question-answering ability
- Numbers of Evaluation Categories/Subcategories: 3/26
- Evaluation Category: Natural science, Social science, Language science
TheoremQA 2023-5 | All | EN | HG | Paper | github | 데이터 세트
- 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 | 모두 | 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 종이
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 종이
- 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 | 데이터 세트
- Publisher: MBZUAI et al.
- Size: 14575 instances
- License: CC-BY-NC-SA-4.0
- Question Type: OQ
- Evaluation Method: CE
- Focus: Multi-task language understanding benchmark for the Arabic language
- Numbers of Evaluation Categories/Subcategories: 5/40
- Evaluation Category: STEM, Social science, Humanities, Language, Other
PersianMMLU 2024-4 | All | FA | HG | Paper | 데이터 세트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: New York University et al.
- Size: 8 datasets
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Natural language understanding capability
- Numbers of Evaluation Categories/Subcategories: 4/8
- Evaluation Category: Word sense disambiguation, Natural language inference, Coreference resolution, Question answering
MCTS 2023-6 | All | ZH | HG | Paper | github
- Publisher: Beijing Language and Culture University
- Size: 723 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Text simplification ability
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Text simplification
RAFT 2021-9 | All | EN | HG & CI | Paper | Dataset | 웹 사이트
- 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 | 모두 | 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 | 데이터 세트
- 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 | 데이터 세트
- 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 | 종이
- 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

추리

Chain-of-Thought Hub 2023-5 | All | EN | CI | Paper | github
- Publisher: University of Edinburgh et al.
- Size: -
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: The multi-step reasoning capabilities
- Numbers of Evaluation Categories/Subcategories: 6/8
- Evaluation Category: Math, Science, Symbolic, Knowledge, Coding, Factual
Choice-75 2023-9 | All | EN | HG & CI & MC | Paper | github
- Publisher: University of Pittsburgh et al.
- Size: 650 instances
- License: -
- Question Type: OQ
- Evaluation Method: CE
- Focus: Predict decisions based on descriptive scenarios
- Numbers of Evaluation Categories/Subcategories: 4/-
- Evaluation Category: Easy, Medium, Hard, N/A
NeuLR 2023-6 | All | EN | CI | Paper | github | 데이터 세트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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

지식

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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- Publisher: LMSYS
- Size: -
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: Long text task capability
- Numbers of Evaluation Categories/Subcategories: 2/-
- Evaluation Category: Coarse-grained topic retrieval, Fine-grained line retrieval
InfiniteBench 2023-11 | All | EN & ZH | HG & CI & MC | Github | Dataset
- Publisher: Tsinghua University et al.
- Size: 3932 instances
- License: Apache-2.0
- Question Type: Multi
- Evaluation Method: -
- Focus: Long text task capability
- Numbers of Evaluation Categories/Subcategories: 5/12
- Evaluation Category: Mathematics, Code, Dialogue, Books, Retrieval
ZeroSCROLLS 2023-5 | All | EN | HG & CI | Paper | Github | Dataset | 웹 사이트
- 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.
- Size: -
- 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
- Size: -
- 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.
- Size: -
- 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

도구

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 | 웹 사이트
- Publisher: UC Berkeley et al.
- Size: 16450 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: The reasoning ability for calling APIs
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: API call
ToolBench 2023-5 | All | EN | HG & CI | Paper | github
- Publisher: SambaNova Systems et al.
- Size: 795 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: The enhancement in tool manipulation for real-world software tasks
- Numbers of Evaluation Categories/Subcategories: 8/-
- Evaluation Category: Open weather, The cat API, Home search, Trip booking, Google sheets, Virtual home, Web shop, Tabletop
ToolEyes 2024-1 | All | EN | HG | Paper | Github | 데이터 세트
- 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

대리인

AgentBench 2023-8 | All | EN | HG & CI & MC | Paper | Github | 웹 사이트
- 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
- Size: -
- 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

암호

BIRD 2023-5 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | 웹 사이트
- 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 | 웹 사이트
- 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 | All | EN & PL | HG | Paper | Github | Dataset
- Publisher: UC Berkeley et al.
- Size: 10000 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: The ability to take an arbitrary natural language specification and generate satisfactory Python code
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Code generation
DomainEval 2024-8 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | 웹 사이트
- 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.
- Size: -
- 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 | 데이터 세트
- 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

법

LAiW 2023-10 | Partial | ZH | CI | Paper | github
- Publisher: Sichuan University et al.
- Size: -
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Legal capabilities
- Numbers of Evaluation Categories/Subcategories: 3/13
- Evaluation Category: Basic legal NLP, Basic legal application, Complex legal application
LawBench 2023-9 | All | ZH | HG & CI | Paper | Github | Dataset
- Publisher: Nanjing University et al.
- Size: -
- 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 | 웹 사이트
- 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/-

의료

CBLUE 2022-5 | All | ZH | HG & CI | Paper | github
- Publisher: Zhejiang University et al.
- Size: 195820 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: Chinese biomedical language understanding
- Numbers of Evaluation Categories/Subcategories: 5/8
- Evaluation Category: Information extraction from the medical text, normalization of the medical term, medical text classification, medical sentence similarity estimation, medical QA
CMB 2023-8 | All | ZH | HG | Paper | Github | Dataset | 웹 사이트
- Publisher: The Chinese University of Hong Kong et al.
- Size: 281047 instances
- License: Apache-2.0
- Question Type: Multi
- Evaluation Method: HE & CE & ME
- Focus: The performance of LLMs in the field of medicine
- Numbers of Evaluation Categories/Subcategories: 2/7
- Evaluation Category: CMB-Exam, CMB-Clin
HuaTuo26M-test 2023-5 | All | ZH | CI | Paper | Github | Dataset
- Publisher: The Chinese University of Hong Kong et al.
- Size: 6000 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: Understand and generate complex medical language
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: Medical consultant records, Encyclopedias, Knowledge bases
MultiMedQA 2022-12 | All | EN | HG & CI | Paper | Dataset
- Publisher: Google Research et al.
- Size: 212822 instances
- License: -
- Question Type: Multi
- Evaluation Method: HE & CE
- Focus: The performance in medical and clinical applications
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Medical question answering
PromptCBLUE 2023-4 | All | ZH | CI | github
- Publisher: East China Normal University et al.
- Size: 20640 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: The performance in Chinese medical scenarios
- Numbers of Evaluation Categories/Subcategories: 16/-
- Evaluation Category: Medical named entity recognition, Medical entity relation extraction, Medical event extraction, etc.
QiZhenGPT_eval 2023-5 | All | ZH | HG | Github | 데이터 세트
- 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.
- Size: -
- 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

재정적인

BBF-CFLEB 2023-2 | All | ZH | HG & CI | Paper | Github | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 종이
- 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 | 웹 사이트
- Publisher: Toyota Technological Institute et al.
- Size: -
- 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 | 웹 사이트
- 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

평가

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.
- Size: -
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Stanford University et al.
- Size: -
- 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 | 데이터 세트
- 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

다국어

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 | 웹 사이트
- Publisher: Carnegie Mellon University et al.
- Size: -
- 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

다른

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 | 웹 사이트
- 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 | 웹 사이트
- 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.
- Size: -
- 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 | 웹 사이트
- Publisher: University of Illinois Urbana-Champaign et al.
- Train/Dev/Test/All Size: 25588/3000/7000/35588
- License: CC-BY-4.0
CondaQA 2022-11 | EN | Paper | Github | Dataset
- Publisher: Carnegie Mellon University et al.
- Train/Dev/Test/All Size: 5832/1110/7240/14182
- License: Apache-2.0
PubMedQA 2019-9 | EN | Paper | Github | Dataset | 웹 사이트
- 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 | 웹 사이트
- Publisher: Carnegie Mellon University
- Train/Dev/Test/All Size: 87866/4887/4934/97687
- License: -
C3 2019-4 | ZH | Paper | Github | 웹 사이트
- Publisher: Cornell University et al.
- Train/Dev/Test/All Size: 11869/3816/3892/19577
- License: -
ReClor 2020-2 | EN | Paper | 웹 사이트
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
DREAM 2020-2 | EN | Paper | Github | 웹 사이트
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
QuAIL 2020-4 | EN | Paper | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Johns Hopkins University et al.
- Train/Dev/Test/All Size: 100730/10000/10000/120730
- License: -
QuAC 2018-8 | EN | Paper | Dataset | 웹 사이트
- 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 | 데이터 세트
- Publisher: University College London
- Train/Dev/Test/All Size: 30000/3000/3000/36000
- License: MIT
Quoref 2019-8 | EN | Paper | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Stanford University
- Train/Dev/Test/All Size: -/-/-/127K
- License: -
QASPER 2021-5 | EN | Paper | 웹 사이트
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: -/-/-/5049
- License: CC-BY-4.0
DuoRC 2018-7 | EN | Paper | Dataset | 웹 사이트
- 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 | 웹 사이트
- Publisher: AI2
- Train/Dev/Test/All Size: 3370/869/3548/7787
- License: CC-BY-SA
CommonsenseQA 2018-11 | EN | Paper | Github | Dataset | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Universidade da Coruna
- Train/Dev/Test/All Size: 2657/1366/2742/13530
- License: MIT
SciQ 2017-9 | EN | Paper | Dataset | 웹 사이트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- Publisher: Tel Aviv University et al.
- Train/Dev/Test/All Size: 2290/-/490/2780
- License: MIT
COPA 2011-6 | EN | Paper | 웹 사이트
- 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 | 웹 사이트
- Publisher: AI2
- Train/Dev/Test/All Size: 2696/384/784/3864
- License: CC-BY-4.0
WIQA 2019-9 | EN | Paper | Dataset | 웹 사이트
- Publisher: AI2
- Train/Dev/Test/All Size: 29808/6894/3993/40695
- License: -
QASC 2019-10 | EN | Paper | Dataset | 웹 사이트
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: 8134/926/920/9980
- License: CC-BY-4.0
QuaRel 2018-11 | EN | Paper | 웹 사이트
- Publisher: AI2
- Train/Dev/Test/All Size: 1941/278/552/2771
- License: CC-BY-4.0
ROPES 2019-8 | EN | Paper | Dataset | 웹 사이트
- Publisher: AI2
- Train/Dev/Test/All Size: 10K/1.6K/1.7K/13.3K
- License: CC-BY-4.0
CREAK 2021-9 | EN | Paper | github
- Publisher: The University of Texas at Austin
- Train/Dev/Test/All Size: 10176/1371/1371/13418
- License: MIT

Recognizing Textual Entailment

The primary objective of tasks related to Recognizing Textual Entailment (RTE) is to assess whether information in one textual segment can be logically inferred from another.

ANLI 2019-10 | EN | Paper | Github | Dataset
- Publisher: UNC Chapel Hill et al.
- Train/Dev/Test/All Size: 162865/3200/3200/169265
- License: CC-NC-4.0
RTE - | EN | Paper1 | Paper2 | Paper3 | Paper4 | Dataset
- Publisher: The PASCAL Recognising Textual Entailment Challenge
- Train/Dev/Test/All Size: 2.49K/277/3K/5.77K
- License: CC-BY-4.0
WANLI 2022-1 | EN | Paper | Dataset
- Publisher: University of Washington et al.
- Train/Dev/Test/All Size: 102885/-/5000/107885
- License: CC-BY-4.0
MedNLI 2018-8 | EN | Paper | Github | Dataset | 웹 사이트
- 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: -

수학

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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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: -

감정 분석

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 | 종이
- 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 | 웹 사이트
- Publisher: Google Research et al.
- Train/Dev/Test/All Size: 5749/1500/1379/8628
- License: -
AFQMC 2020-12 | ZH | 종이
- 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 | 종이
- 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

텍스트 생성

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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 데이터 세트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 종이
- 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 | 웹 사이트
- Publisher: Tsinghua University
- Train/Dev/Test/All Size: -/-/-/1672165
- License: MIT
CSLDCP 2021-7 | ZH | Paper | Github | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- Publisher: Westlake University
- Train/Dev/Test/All Size: -/-/-/10181
- License: CC-BY-SA-4.0
Spider 2018-9 | EN & PL | Paper | Github | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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 | 웹 사이트
- 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

서류

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

원격 감지

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

일반적인

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

주제

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

의료

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

연락하다

Contact information：

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!

소환

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}
}

확장하다

Awesome LLMs Datasets

굉장한 선수-다타타 세트

종이

데이터 세트 정보 모듈

changelog

목차

사전 훈련 Corpora

일반 사전 훈련 코포라

웹 페이지

언어 텍스트

서적

학업 자료

암호

평행 코퍼스

소셜 미디어

백과 사전

다중 범주

도메인 별 사전 훈련 Corpora

재정적인

의료

수학

다른

명령 미세 조정 데이터 세트

일반 지침 미세 조정 데이터 세트

인간 생성 데이터 세트 (HG)

모델 구성 데이터 세트 (MC)

기존 데이터 세트 수집 및 개선 (CI)

HG & CI

HG & MC

CI & MC

HG & CI & MC

Domain-specific Instruction Fine-tuning Datasets

의료

암호

합법적인

수학

교육

다른

Preference Datasets

Preference Evaluation Methods

투표

종류

점수

다른

Evaluation Datasets

일반적인

시험

주제

NLU

추리

지식

Long Text

도구

대리인

암호

OOD

법

의료

재정적인

Social Norms

Factuality

평가

Multitask

다국어

다른

Evaluation Platform

Traditional NLP Datasets

Question Answering

Reading Comprehension

Selection & Judgment

Cloze Test

Answer Extraction

Unrestricted QA

Knowledge QA

Reasoning QA

Recognizing Textual Entailment

수학

Coreference Resolution

감정 분석

Semantic Matching