Awesome LLMs Datasets Download - Awesome LLMs Datasets Quellcode Download

Awesome-Llms-Datensätze

Fassen Sie vorhandene repräsentative LLMS-Textdatensätze in fünf Dimensionen zusammen: Voraussetzungskorpora, Feinabstimmungsdatensätze, Präferenzdatensätze, Bewertungsdatensätze und herkömmliche NLP-Datensätze . (Regelmäßige Updates)
Es wurden neue Datensatzabschnitte hinzugefügt: Multi-Modal Language-Modelle (MLLMS) -Datensätze (Multi-Modal-Modelle), RAG-Datensätze (Abrufener Augmented Generation) . (Schrittweise Updates)

Papier

Das Papier "Datensätze für Großsprachmodelle: Eine umfassende Umfrage" wurde veröffentlicht. （2024/2）

Abstrakt:

In diesem Artikel wird eine Erkundung in das LLM -Datensätzen (Langual Language Model) untersucht, die bei den bemerkenswerten Fortschritten von LLMs eine entscheidende Rolle spielen. Die Datensätze dienen als grundlegende Infrastruktur analog zu einem Wurzelsystem, das die Entwicklung von LLMs erhält und fördert. Infolgedessen erscheint die Untersuchung dieser Datensätze als kritisches Thema in der Forschung. Um das aktuelle Fehlen eines umfassenden Überblicks und eine gründliche Analyse von LLM-Datensätzen zu beheben und um Einblicke in ihren aktuellen Status und zukünftigen Trends zu gewinnen, konsolidiert diese Umfrage und kategorisiert die grundlegenden Aspekte von LLM-Datensätzen aus fünf Perspektiven: (1) Pre- Trainingskorpora; (2) Befehl feinabstimmungsdatensätze; (3) Präferenzdatensätze; (4) Bewertungsdatensätze; (5) Datensätze für traditionelle natürliche Sprachverarbeitung (NLP). Die Umfrage beleuchtet die vorherrschenden Herausforderungen und weist potenzielle Wege für zukünftige Untersuchungen auf. Darüber hinaus wird auch eine umfassende Überprüfung der vorhandenen verfügbaren Datensatzressourcen bereitgestellt, einschließlich Statistiken aus 444 Datensätzen, die 8 Sprachkategorien abdecken und 32 Domänen überspannten. Informationen aus 20 Dimensionen sind in die Datensatzstatistik aufgenommen. Die untersuchte Gesamtdatengröße übertrifft 774,5 TB für Voraussetzungen und 700-m-Instanzen für andere Datensätze. Wir wollen die gesamte Landschaft von LLM -Textdatensätzen präsentieren, die als umfassende Referenz für Forscher in diesem Bereich dienen und zu zukünftigen Studien beitragen.

Abb. 1. Die Gesamtarchitektur der Umfrage. Zoom für eine bessere Sichtweise

Datensatzinformationsmodul

Das Folgende ist eine Zusammenfassung des Datensatzinformationsmoduls.

Corpus/Datensatzname
Herausgeber
Freigabezeit
- "X" zeigt einen unbekannten Monat an.
Größe
Öffentlich oder nicht
- "Alles" zeigt eine vollständige Open Source an;
- "Teilweise" zeigt teilweise Open Source an;
- "Nicht" zeigt nicht Open Source an.
Lizenz
Sprache
- "En" zeigt Englisch an;
- "Zh" zeigt Chinesen an;
- "AR" zeigt Arabisch an;
- "Es" zeigt Spanisch an;
- "Ru" zeigt Russisch an;
- "De" zeigt Deutsch an;
- "KO" zeigt Koreanisch an;
- "LT" zeigt Litauen an;
- "FA" zeigt Persisch/Farsi an;
- "PL" zeigt die Programmiersprache an;
- "Multi" zeigt mehrsprachig an, und die Anzahl in Klammern zeigt die Anzahl der enthaltenen Sprachen an.
Konstruktionsmethode
- "Hg" zeigt den menschlichen Corpus/Datensatz an;
- "MC" zeigt das Modell konstruiert Corpus/Datensatz an.
- "CI" zeigt die Sammlung und Verbesserung des vorhandenen Corpus/Datensatzes an.
Kategorie
Quelle
Domain
Anweisungskategorie
Präferenzbewertungsmethode
- "Vo" zeigt die Abstimmung an;
- "Also" zeigt die Sortierung an;
- "SC" zeigt die Punktzahl an;
- "-H" zeigt von Menschen durchgeführt;
- "-M" zeigt an, dass von Modellen durchgeführt wird.
Fragetyp
- "SQ" zeigt subjektive Fragen an;
- "OQ" zeigt objektive Fragen an;
- "Multi" gibt mehrere Fragetypen an.
Bewertungsmethode
- "CE" zeigt die Code -Bewertung an;
- "Er" zeigt die menschliche Bewertung an;
- "ME" zeigt die Modellbewertung an.
Fokus
Anzahl der Bewertungskategorien/Unterkategorien
Bewertungskategorie
Anzahl der Entitätskategorien (NER -Aufgabe)
Anzahl der Beziehungskategorien (RE -Aufgabe)

Changelog

（2024/01/17） Erstellen Sie das Dataset-Repository von Awesome-lms-Datasets .
（2024/02/02） Informationen für einige Datensätze überarbeiten; Fügen Sie Dolma hinzu (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Mehrkategorie).
（2024/02/15） AYA-Sammlung hinzufügen (Anweisungen Feinabstimmungsdatensätze | Allgemeine Anweisung Feinabstimmungsdatensätze | HG & CI & MC); AYA-Datensatz (Befehl Feinabstimmungsdatensätze | Allgemeine Anweisung Feinabstimmungsdatensätze | HG).
（2024/02/22） OpenMathinstruct-1 hinzufügen (Befehl feine Tuning-Datensätze | Domänenspezifische Befehl Fine-Tuning-Datensätze | Math); Finben (Evaluierungsdatensätze | Finanz).
（2024/04/05）
- Fügen Sie neue Datensatzabschnitte hinzu: (1) Datensätze für multimodale Großsprachenmodelle (MLLMS); (2) Datensätze (Abrufener Augmented Generation (RAG) .
- Fügen Sie MMRS-1M hinzu (MLLMS-Datensätze | Befehl feinabstimmungsdatensätze); VideoChat2-it (MLLMS-Datensätze | Befehl feinabstimmungsdatensätze); Unterrichtdoc (MLLMS-Datensätze | Befehl feinabstimmungsdatensätze); AllAVA-4V-Daten (MLLMS-Datensätze | Befehl Fine-Tuning-Datensätze); MVBench (MLLMS -Datensätze | Evaluierungsdatensätze); Olympiadbench (MLLMS -Datensätze | Evaluierungsdatensätze); MMMU (MLLMS -Datensätze | Evaluierungsdatensätze).
- Hinzufügen von Hinweise auf die Benchmark -Serie (Evaluierungsdatensätze | Evaluierungsplattform); Openllm Bohrlochboard (Evaluierungsdatensätze | Evaluierungsplattform); OpenCompass (Evaluierungsdatensätze | Evaluierungsplattform); MTEB Bohrlochboard (Evaluation -Datensätze | Evaluierungsplattform); C-MTEB-Rangliste (Evaluierungsdatensätze | Evaluierungsplattform).
- NaH hinzufügen (Nadel-in-a-hayStack) (Evaluierungsdatensätze | Long Text); Tooleyes (Bewertungsdatensätze | Tool); Uhgeval (Evaluierungsdatensätze | Tatsachen); Clongeval (Evaluierungsdatensätze | langer Text).
- Mathpile hinzufügen (Voraussetzungskorpora | domänenspezifische Vorausbildungskorpora | Math); Wanjuan-CC (Voraussetzungskorpora | General Pre-Training Corpora | Webseiten).
- Iepile hinzufügen (Anweisungen Feinabstimmungsdatensätze | Allgemeine Anweisung Feinabstimmungsdatensätze | CI); Anweisung (Anweisung Feinabstimmungsdatensätze | Allgemeine Anweisung Feinabstimmungsdatensätze | Hg).
- Crud-Rag (RAG-Datensätze) hinzufügen; Wikieval (Rag -Datensätze); RGB (RAG -Datensätze); RAG-Instruct-Benchmark-Tester (Rag-Datensätze); ARES (RAG -Datensätze).
（2024/04/06）
- GPQA hinzufügen (Evaluierungsdatensätze | Subjekt); MGSM (Evaluierungsdatensätze | Mehrsprachiger); Halueval-wild (Evaluierungsdatensätze | Tatsachen); CMATH (Evaluierungsdatensätze | Subjekt); Finemath (Evaluierungsdatensätze | Subjekt); Echtzeit -QA (Evaluierungsdatensätze | Fakten); WYWeb (Evaluierungsdatensätze | Subjekt); Chinesische FakteVal (Evaluierungsdatensätze | Fakten); Zählstars (Evaluierungsdatensätze | langer Text).
- Slimpajama hinzufügen (Voraussetzungskorpora | Allgemeine Vorausbildungskorpora | Mehrkategorie); MassiveText (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Mehrkategorie); Madlad-400 (Voraussetzungskorpora | General Pre-Training Corpora | Webseiten); Minerva (Voraussetzungskorpora | Allgemeine Vorausbildungskörper | Mehrkategorie); Ccaligned (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Parallel Corpus); Wikimatrix (Vorauslaufkorpora | Allgemeine Vorausbildungskörper | Parallel Corpus); OpenWebmath (Voraussetzungskorpora | Domänenspezifische Voraussetzungskorpora | Math).
- Fügen Sie Webquestions hinzu (herkömmliche NLP -Datensätze | Fragenbeantwortung | Wissens -QA).
- Fügen Sie Alce (RAG -Datensätze) hinzu.
- Alphafin hinzufügen (Befehl feinabstimmungsdatensätze | Domänenspezifische Befehl feinabstimmungsdatensätze | Andere); Coig-cqia (Befehl feine abstimmende Datensätze | Allgemeine Anweisung Feinabstimmungsdatensätze | HG & CI).
（2024/06/15）
- Hinzufügen hinzufügen (Evaluierungsdatensätze | medizinisch); CHC-Bench (Evaluierungsdatensätze | Allgemein); CIF-Bench (Evaluierungsdatensätze | Allgemein); ACLUE (Evaluierungsdatensätze | Subjekt); LESC (Evaluierungsdatensätze | NLU); Alignbench (Evaluierungsdatensätze | Multitask); Sciknoweval (Evaluierungsdatensätze | Subjekt).
- MAP-CC addieren (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Mehrkategorie); Feinweb (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Webseiten); CCI 2.0 (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Webseiten).
- Fügen Sie Wildchat hinzu (Anweisungen Feinabstimmungsdatensätze | MC).
- Openhermespreferences hinzufügen (Präferenzdatensätze | Sortieren); huozi_rlhf_data (Präferenzdatensätze | Abstimmung); HelpSter (Präferenzdatensätze | Punktzahl); HelpSter2 (Präferenzdatensätze | Punktzahl).
- MMT-Bench (MLLMS-Datensätze | Evaluierungsdatensätze) hinzufügen; MOSCAR (MLLMS-Datensätze | Vorausbildung Corpora); MM-NIAH (MLLMS-Datensätze | Evaluierungsdatensätze).
- Fügen Sie Crag (Lag -Datensätze) hinzu.
（2024/08/29）
- GameBench hinzufügen (Bewertungsdatensätze | Argumentation); Halludial (Evaluierungsdatensätze | Fakten); Wildbench (Evaluierungsdatensätze | Allgemein); DomaineVal (Evaluierungsdatensätze | Code); Sysbench (Evaluierungsdatensätze | Allgemein); Kobest (Bewertungsdatensätze | nlu); Sarkasmbench (Evaluierungsdatensätze | NLU); C ³ Bank (Bewertungsdatensätze | Betreff); TableBench (Bewertungsdatensätze | Argumentation); Ackergaleval (Bewertungsdatensätze | Gesetz).
- Multitrust hinzufügen (MLLMS -Datensätze | Bewertungsdatensätze); OBELISC (MLLMS-Datensätze | Voraus trainierende Korpora); Multimed (MLLMS -Datensätze | Bewertungsdatensätze).
- Fügen Sie DCLM hinzu (Voraussetzungskorpora | Allgemeine Voraussetzungskorpora | Webseiten).
- Fügen Sie litauanisch-qa-v1 hinzu (Befehl feinabstimmungsdatensätze | ci & mc); Rückstruktur (Befehl feinabstimmungsdatensätze | Hg & CI & MC); Kollm-Converations (Befehl Feinabstimmungsdatensätze | CI).
（2024/09/04）
- Fügen Sie Longwriter-6K hinzu (Befehl feinabstimmungsdatensätze | CI & MC).
- Fügen Sie Medtrinity-25m hinzu (MLLMS-Datensätze | Bewertungsdatensätze); MMIU (MLLMS -Datensätze | Evaluierungsdatensätze).
- Fügen Sie Expository-Prose-V1 hinzu (Voraussetzungskorpora | Allgemeine Vor-Training-Korpora | Mehrkategorie).
- DebateQA hinzufügen (Bewertungsdatensätze | Wissen); NadelBench (Evaluierungsdatensätze | langer Text); Arabicmmlu (Evaluierungsdatensätze | Subjekt); PersianMMLU (Evaluierungsdatensätze | Subjekt); TMMLU+ (Bewertungsdatensätze | Betreff).
- RAGEVAL (RAG -Datensätze) hinzufügen; LFRQA (RAG -Datensätze); Multihop-Rag (Rag-Datensätze).
Wir werden die Datensatzinformationen im CSV -Format veröffentlichen.

Inhaltsverzeichnis

Korpora vor der Ausbildung
- Allgemeine Voraussetzungskorpora
  - Webseiten
  - Sprachtexte
  - Bücher
  - Akademische Materialien
  - Code
  - Parallelkorpus
  - Social Media
  - Enzyklopädie
  - Mehrkategorie
- Domänenspezifische Voraussetzungskorpora
  - Finanziell
  - Medizinisch
  - Mathe
  - Andere
Anweisungen Feinabstimmungsdatensätze
- Allgemeine Anweisungen Feinabstimmungsdatensätze
  - Menschlich erzeugte Datensätze (HG)
  - Modell konstruierte Datensätze (MC)
  - Sammlung und Verbesserung vorhandener Datensätze (CI)
  - HG & CI
  - HG & MC
  - CI & MC
  - HG & CI & MC
- Domänenspezifische Anweisung Feinabstimmungsdatensätze
  - Medizinisch
  - Code
  - Legal
  - Mathe
  - Ausbildung
  - Andere
Präferenzdatensätze
- Präferenzbewertungsmethoden
  - Abstimmung
  - Sortieren
  - Punktzahl
  - Andere
Bewertungsdatensätze
- Allgemein
- Prüfung
- Thema
- NLU
- Argumentation
- Wissen
- Langer Text
- Werkzeug
- Agent
- Code
- Ood
- Gesetz
- Medizinisch
- Finanziell
- Soziale Normen
- Tatsache
- Auswertung
- Multitasking
- Mehrsprachig
- Andere
- Bewertungsplattform
Traditionelle NLP -Datensätze
- Frage Beantwortung
  - Leseverständnis
    - Auswahl und Urteilsvermögen
    - Lückentest
    - Antwortextraktion
    - Uneingeschränkte QA
  - Wissen QA
  - Argumentation QA
- Erkennen von Textbeschaffungen
- Mathe
- Koreferenzauflösung
- Stimmungsanalyse
- Semantische Matching
- Textgenerierung
- Textübersetzung
- Textübersicht
- Textklassifizierung
- Textqualitätsbewertung
- Text-to-Code
- Genannte Entitätserkennung
- Beziehungsextraktion
- Multitasking
MLLLMS-Datensätze (Multi-Modal großer Sprache)
- Korpora vor der Ausbildung
- Anweisungen Feinabstimmungsdatensätze
- Bewertungsdatensätze
RAB -Datensätze (Abrufener Augmented Generation) abrufen

Korpora vor der Ausbildung

Die Voraussetzungskorpora sind große Sammlungen von Textdaten, die während des Vorausgangsprozesses von LLMs verwendet werden.

Allgemeine Voraussetzungskorpora

Die allgemeinen Voraussetzungskorpora sind groß angelegte Datensätze aus umfangreichem Text aus verschiedenen Domänen und Quellen. Ihr Hauptmerkmal ist, dass der Textinhalt nicht auf eine einzige Domäne beschränkt ist, wodurch sie für die Schulung allgemeiner Grundmodelle geeigneter werden. Korpora werden basierend auf Datenkategorien eingestuft.

Datensatzinformatformat:

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

Webseiten

CC-Stories 2018-6 | Nicht | En | Ci | Papier | Github | Datensatz
- Verlag: Google Brain
- Größe: 31 GB
- Lizenz: -
- Quelle: Common Crawl
CC100 2020-7 | Alle | Multi (100) | Ci | Papier | Datensatz
- Verlag: Facebook AI
- Größe: 2,5 TB
- Lizenz: Häufige Crawl -Nutzungsbedingungen
- Quelle: Common Crawl
Cluecorpus2020 2020-3 | Alle | Zh | Ci | Papier | Datensatz
- Verlag: Hinweisorganisation
- Größe: 100 GB
- Lizenz: MIT
- Quelle: Common Crawl
Common Crawl 2007-X | Alle | Multi | Hg | Webseite
- Verlag: Common Crawl
- Größe: -
- Lizenz: Häufige Crawl -Nutzungsbedingungen
- Quelle: Web -Crawler -Daten
Culturax 2023-9 | Alle | Multi (167) | Ci | Papier | Datensatz
- Verlag: Universität von Oregon et al.
- Größe: 27 TB
- Lizenz: MC4 & Oscar Lizenz
- Quelle: MC4, Oscar
C4 2019-10 | Alle | En | Ci | Papier | Datensatz
- Verlag: Google Research
- Größe: 12,68 TB
- Lizenz: ODC-by & Common Crawl-Nutzungsbedingungen
- Quelle: Common Crawl
MC4 2021-6 | Alle | Multi (108) | Ci | Papier | Datensatz
- Verlag: Google Research
- Größe: 251 GB
- Lizenz: ODC-by & Common Crawl-Nutzungsbedingungen
- Quelle: Common Crawl
Oscar 22.01 2022-1 | Alle | Multi (151) | Ci | Papier | Datensatz | Webseite
- Verlag: Inria
- Größe: 8,41 TB
- Lizenz: CC0
- Quelle: Common Crawl
RealNews 2019-5 | Alle | En | Ci | Papier | Github
- Verlag: University of Washington et al.
- Größe: 120 GB
- Lizenz: Apache-2.0
- Quelle: Common Crawl
Redpajama-V2 2023-10 | Alle | Multi (5) | Ci | Github | Datensatz | Webseite
- Verlag: zusammen Computer
- Größe: 30,4 t Tokens
- Lizenz: Häufige Crawl -Nutzungsbedingungen
- Quelle: Common Crawl, C4 usw.
Raffinedweb 2023-6 | Partiell | En | Ci | Papier | Datensatz
- Verlag: Das Falcon LLM -Team
- Größe: 5000 GB
- Lizenz: ODC-by-1.0
- Quelle: Common Crawl
Wudaocorpora-text 2021-6 | Partiell | Zh | Hg | Papier | Datensatz
- Verlag: Baai et al.
- Größe: 200 GB
- Lizenz: MIT
- Quelle: Chinesische Webseiten
Wanjuan-CC 2024-2 | Partiell | En | Hg | Papier | Datensatz
- Verlag: Shanghai Artifcial Intelligence Laboratory
- Größe: 1 t Tokens
- Lizenz: CC-BY-4.0
- Quelle: Common Crawl
Madlad-400 2023-9 | Alle | Multi (419) | Hg | Papier | Github | Datensatz
- Verlag: Google Deepmind et al.
- Größe: 2,8 t Tokens
- Lizenz: ODL-by
- Quelle: Common Crawl
Feinweb 2024-4 | Alle | En | Ci | Datensatz
- Verlag: Huggingfacefw
- Größe: 15 TB Tokens
- Lizenz: ODC-by-1.0
- Quelle: Common Crawl
CCI 2.0 2024-4 | Alle | Zh | Hg | Datensatz1 | Datensatz2
- Verlag: Baai
- Größe: 501 GB
- Lizenz: CCI -Nutzungsvergrößerung
- Quelle: Chinesische Webseiten
DCLM 2024-6 | Alle | En | Ci | Papier | Github | Datensatz | Webseite
- Verlag: University of Washington et al.
- Größe: 279,6 TB
- Lizenz: Häufige Crawl -Nutzungsbedingungen
- Quelle: Common Crawl

Sprachtexte

ANC 2003-X | Alle | En | Hg | Webseite
- Verlag: Die US National Science Foundation et al.
- Größe: -
- Lizenz: -
- Quelle: Amerikanische englische Texte
BNC 1994-X | Alle | En | Hg | Webseite
- Verlag: Oxford University Press et al.
- Größe: 4124 Texte
- Lizenz: -
- Quelle: Britische englische Texte
News-Crawl 2019-1 | Alle | Multi (59) | Hg | Datensatz
- Verlag: Ukri et al.
- Größe: 110 GB
- Lizenz: CC0
- Quelle: Zeitungen

Bücher

Annas Archiv 2023-X | Alle | Multi | Hg | Webseite
- Verlag: Anna
- Größe: 586,3 TB
- Lizenz: -
- Quelle: Sci-Hub, Bibliothek Genesis, Z-Library usw.
Bookcorpusopen 2021-5 | Alle | En | Ci | Papier | Github | Datensatz
- Verlag: Jack Bandy et al.
- Größe: 17.868 Bücher
- Lizenz: Smashwords -Nutzungsbedingungen
- Quelle: Toronto Book Corpus
PG-19 2019-11 | Alle | En | Hg | Papier | Github | Datensatz
- Verlag: DeepMind
- Größe: 11,74 GB
- Lizenz: Apache-2.0
- Quelle: Project Gutenberg
Projekt Gutenberg 1971-X | Alle | Multi | Hg | Webseite
- Verlag: Ibiblio et al.
- Größe: -
- Lizenz: Das Projekt Gutenberg
- Quelle: E -Book -Daten
Smashwords 2008-X | Alle | Multi | Hg | Webseite
- Verlag: Draft2digital et al.
- Größe: -
- Lizenz: Smashwords -Nutzungsbedingungen
- Quelle: E -Book -Daten
Toronto Book Corpus 2015-6 | Nicht | En | Hg | Papier | Webseite
- Verlag: Universität von Toronto et al.
- Größe: 11.038 Bücher
- Lizenz: MIT & Smashwords Nutzungsbedingungen
- Quelle: Smashwords

Akademische Materialien

Arxiv 1991-X | Alle | En | Hg | Webseite
- Verlag: Paul Ginsparg et al.
- Größe: -
- Lizenz: Nutzungsbedingungen für Arxiv -APIs
- Quelle: Arxiv Preprint
S2ORC 2020-6 | Alle | En | Ci | Papier | Github
- Verlag: Ai2 et al.
- Größe: 81,1 MB
- Lizenz: ODC-by-1.0
- Quelle: Mag, Arxiv, PubMed usw.

Code

BigQuery 2022-3 | Nicht | Pl | Ci | Papier | Github
- Verlag: Salesforce Research
- Größe: 341,1 GB
- Lizenz: Apache-2.0
- Quelle: BigQuery
Github 2008-4 | Alle | Pl | Hg | Webseite
- Verlag: Microsoft
- Größe: -
- Lizenz: -
- Quelle: Verschiedene Codeprojekte
PHI-1 2023-6 | Nicht | En & pl | HG & MC | Papier | Datensatz
- Verlag: Microsoft Research
- Größe: 7 B Tokens
- Lizenz: CC-mal-nc-sa-3.0
- Quelle: Der Stack, Stackoverflow, GPT-3.5-Generation
Der Stack 2022-11 | Alle | PL (358) | Hg | Papier | Datensatz
- Verlag: Servicenow Research et al.
- Größe: 6 TB
- Lizenz: Die Bedingungen der ursprünglichen Lizenzen
- Quelle: Zulässige Quellcode-Dateien mit lizenziertem Quellcode

Parallelkorpus

MTP 2023-9 | Alle | En & zh | HG & CI | Datensatz
- Verlag: Baai
- Größe: 1,3 TB
- Lizenz: BAAI -Datennutzungsprotokoll
- Quelle: chinesisch-englisch parallele Textpaare im Web
Multiun 2010-5 | Alle | Multi (7) | Hg | Papier | Webseite
- Verlag: Deutsches Forschungszentrum für künstliche Intelligenz (DFKI) GmbHH
- Größe: 4353 MB
- Lizenz: -
- Quelle: Dokumente der Vereinten Nationen
Parakrawl 2020-7 | Alle | Multi (42) | Hg | Papier | Webseite
- Verlag: Prompsit et al.
- Größe: 59996 Dateien
- Lizenz: CC0
- Quelle: Web -Crawler -Daten
Uncorpus v1.0 2016-5 | Alle | Multi (6) | Hg | Papier | Webseite
- Verlag: Vereinte Nationen et al.
- Größe: 799276 Dateien
- Lizenz: -
- Quelle: Dokumente der Vereinten Nationen
Ccaligned 2020-11 | Alle | Multi (138) | Hg | Papier | Datensatz
- Verlag: Facebook AI et al.
- Größe: 392 M URL -Paare
- Lizenz: -
- Quelle: Common Crawl
Wikimatrix 2021-4 | Alle | Multi (85) | Hg | Papier | Github | Datensatz
- Verlag: Facebook AI et al.
- Größe: 134 m parallele Sätze
- Lizenz: cc-by-sa
- Quelle: Wikipedia

Social Media

OpenWebtext 2019-4 | Alle | En | Hg | Webseite
- Verlag: Brown University
- Größe: 38 GB
- Lizenz: CC0
- Quelle: Reddit
Pushshift Reddit 2020-1 | Alle | En | Ci | Papier | Webseite
- Verlag: pushshift.io et al.
- Größe: 2 TB
- Lizenz: -
- Quelle: Reddit
Reddit 2005-6 | Alle | En | Hg | Webseite
- Verlag: Condé Nast Digital et al.
- Größe: -
- Lizenz: -
- Quelle: Social -Media -Beiträge
Stackkexchange 2008-9 | Alle | En | Hg | Datensatz | Webseite
- Verlag: Stack Exchange
- Größe: -
- Lizenz: CC-mal-Sa4.0
- Quelle: Community -Frage- und Beantwortungsdaten
WebText 2019-2 | Partiell | En | Hg | Papier | Github | Datensatz
- Verlag: OpenAI
- Größe: 40 GB
- Lizenz: MIT
- Quelle: Reddit
Zhihu 2011-1 | Alle | Zh | Hg | Webseite
- Verlag: Peking Zhizhe Tianxia Technology Co., Ltd.
- Größe: -
- Lizenz: Zhihu -Benutzervereinbarung
- Quelle: Social -Media -Beiträge

Enzyklopädie

Baidu Baike 2008-4 | Alle | Zh | Hg | Webseite
- Verlag: Baidu
- Größe: -
- Lizenz: Baidu Baike -Benutzervereinbarung
- Quelle: Enzyklopädische Inhaltsdaten
Tigerbot-Wiki 2023-5 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 205 MB
- Lizenz: Apache-2.0
- Quelle: Baidu Baike
Wikipedia 2001-1 | Alle | Multi | Hg | Datensatz | Webseite
- Verlag: Wikimedia Foundation
- Größe: -
- Lizenz: CC-by-Sa-3.0 & GFDL
- Quelle: Enzyklopädische Inhaltsdaten

Mehrkategorie

Arabictext 2022 2022-12 | Alle | Ar | HG & CI | Datensatz
- Verlag: Baai et al.
- Größe: 201.9 GB
- Lizenz: CC-mal-Sa4.0
- Quelle: Arabicweb, Oscar, CC100 usw.
MNBVC 2023-1 | Alle | Zh | HG & CI | Github | Datensatz
- Verlag: Liwu Community
- Größe: 20811 GB
- Lizenz: MIT
- Quelle: Chinesische Bücher, Webseiten, Thesen usw.
Redpajama-V1 2023-4 | Alle | Multi | HG & CI | Github | Datensatz
- Verlag: zusammen Computer
- Größe: 1,2 t Tokens
- Lizenz: -
- Quelle: Common Crawl, Github, Bücher usw.
Wurzeln 2023-3 | Partiell | Multi (59) | HG & CI | Papier | Datensatz
- Verlag: Umarmung Face et al.
- Größe: 1,61 TB
- Lizenz: Bloom Open-Rail-M
- Quelle: Oscar, Github usw.
Der Stapel 2021-1 | Alle | En | HG & CI | Papier | Github | Datensatz
- Verlag: Eleutherai
- Größe: 825.18 GB
- Lizenz: MIT
- Quelle: Bücher, Arxiv, Github usw.
Tigerbot_Pretrain_en 2023-5 | Partiell | En | Ci | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 51 GB
- Lizenz: Apache-2.0
- Quelle: Englische Bücher, Webseiten, En-Wiki usw.
Tigerbot_Pretrain_zh 2023-5 | Partiell | Zh | Hg | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 55 GB
- Lizenz: Apache-2.0
- Quelle: Chinesische Bücher, Webseiten, ZH-Wiki usw.
Wanjuantext-1.0 2023-8 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Shanghai AI Laboratory
- Größe: 1094 GB
- Lizenz: CC-BY-4.0
- Quelle: Webseiten, Enzyklopädie, Bücher usw.
Dolma 2024-1 | Alle | En | HG & CI | Papier | Github | Datensatz
- Verlag: Ai2 et al.
- Größe: 11519 GB
- Lizenz: MR -Vereinbarung
- Quelle: Project Gutenberg, C4, Reddit usw.
Slimpajama 2023-6 | Alle | En | HG & CI | Github | Datensatz | Webseite
- Verlag: Cerebras et al.
- Größe: 627 B Tokens
- Lizenz: -
- Quelle: Common Crawl, C4, Github usw.
Massivext 2021-12 | Nicht | Multi | HG & CI | Papier
- Verlag: Google DeepMind
- Größe: 10,5 TB
- Lizenz: -
- Quelle: Massiveweb, C4, Bücher usw.
Minerva 2022-6 | Nicht | En | Hg | Papier
- Verlag: Google Research
- Größe: 38,5 B Tokens
- Lizenz: -
- Quelle: Arxiv, Webseiten usw.
MAP-CC 2024-4 | Alle | Zh | Hg | Papier | Github | Datensatz | Webseite
- Verlag: Multimodal Art Projection Research Community et al.
- Größe: 840,48 B Token
- Lizenz: CC-mal-NC-ND-4.0
- Quelle: Chinese Common Crawl, Chinesische Enzyklopädien, chinesische Bücher usw. usw.
Expository-Prose-V1 2024-8 | Alle | En | HG & CI | Papier | Github | Datensatz
- Verlag: Pints.ai Labs
- Größe: 56 B Tokens
- Lizenz: MIT
- Quelle: Arxiv, Wikipedia, Gutenberg usw.

Domänenspezifische Voraussetzungskorpora

Domänenspezifische Voraussetzungen sind LLM-Datensätze, die für bestimmte Felder oder Themen angepasst sind. Die Art des Korpus wird typischerweise in der inkrementellen Vor-Trainingsphase von LLMs verwendet. Korpora werden basierend auf Datendomänen eingestuft.

Datensatzinformatformat:

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

Finanziell

BBT-Fincorpus 2023-2 | Partiell | Zh | Hg | Papier | Github | Webseite
- Verlag: Fudan University et al.
- Größe: 256 GB
- Lizenz: -
- Quelle: Unternehmensankündigungen, Forschungsberichte, finanzielle
- Kategorie: Multi
- Domain: Finanzierung
Fincorpus 2023-9 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Du Xiaoman
- Größe: 60,36 GB
- Lizenz: Apache-2.0
- Quelle: Unternehmensankündigungen, Finanznachrichten, Finanzprüfungsfragen
- Kategorie: Multi
- Domain: Finanzierung
Finglm 2023-7 | Alle | Zh | Hg | Github
- Verlag: Wissen Atlas et al.
- Größe: 69 GB
- Lizenz: Apache-2.0
- Quelle: Jahresberichte von börsennotierten Unternehmen
- Kategorie: Sprachtexte
- Domain: Finanzierung
Tigerbot-verdient 2023-5 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 488 MB
- Lizenz: Apache-2.0
- Quelle: Finanzberichte
- Kategorie: Sprachtexte
- Domain: Finanzierung
Tigerbot-Research 2023-5 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 696 MB
- Lizenz: Apache-2.0
- Quelle: Forschungsberichte
- Kategorie: Sprachtexte
- Domain: Finanzierung

Medizinisch

Medical-PT 2023-5 | Alle | Zh | Ci | Github | Datensatz
- Verlag: Ming Xu
- Größe: 632,78 MB
- Lizenz: Apache-2.0
- Quelle: Medizinische Enzyklopädie -Daten, medizinische Lehrbücher
- Kategorie: Multi
- Domain: Medizin
PubMed Central 2000-2 | Alle | En | Hg | Webseite
- Verlag: NCBI
- Größe: -
- Lizenz: PMC Copyright Bekanntmachung
- Quelle: Biomedizinische wissenschaftliche Literatur
- Kategorie: Akademische Materialien
- Domain: Medizin

Mathe

Proof-Pile-2 2023-10 | Alle | En | HG & CI | Papier | Github | Datensatz | Webseite
- Verlag: Princeton University et al.
- Größe: 55 B Tokens
- Lizenz: -
- Quelle: Arxiv, OpenWebmath, Algebraicstack
- Kategorie: Multi
- Domain: Mathematik
MATHPILE 2023-12 | Alle | En | Hg | Papier | Github | Datensatz
- Verlag: Shanghai Jiao Tong University et al.
- Größe: 9,5 B Tokens
- Lizenz: CC-mal-nc-sa4.0
- Quelle: Lehrbücher, Wikipedia, Proofwiki, Commoncrawl, Stackexchange, Arxiv
- Kategorie: Multi
- Domain: Mathematik
OpenWebmath 2023-10 | Alle | En | Hg | Papier | Github | Datensatz
- Verlag: Universität von Toronto et al.
- Größe: 14,7 B -Token
- Lizenz: ODC-by-1.0
- Quelle: Common Crawl
- Kategorie: Webseiten
- Domain: Mathematik

Andere

Tigerbot-Law 2023-5 | Alle | Zh | Hg | Papier | Github | Datensatz
- Verlag: Tigerbot
- Größe: 29,9 MB
- Lizenz: Apache-2.0
- Quelle: Rechtsklauseln
- Kategorie: Sprachtexte
- Domain: Gesetz
Transgpt-PT 2023-7 | Alle | Zh | Hg | Github | Datensatz
- Verlag: Peking Jiaotong University
- Größe: 35,8 MB
- Lizenz: Apache-2.0
- Quelle: Technologiedokumente, Ingenieurbauinformationen, statistische Daten usw.
- Kategorie: Multi
- Domain: Transport

Anweisungen Feinabstimmungsdatensätze

Die Befehlsfeinabeinstellungsdatensätze bestehen aus einer Reihe von Textpaaren, die „Anweisungseingänge“ und „Antwortausgänge“ umfassen. "Anweisungseingänge" stellen Anfragen dar, die vom Menschen an das Modell gestellt werden. Es gibt verschiedene Arten von Anweisungen, wie Klassifizierung, Zusammenfassung, Paraphrasierung usw. „Ausgänge beantworten“ sind die Antworten, die vom Modell nach der Anweisung und dem Einrichten der menschlichen Erwartungen generiert werden.

Allgemeine Anweisungen Feinabstimmungsdatensätze

Allgemeine Anweisungen Feinabstimmungsdatensätze enthalten eine oder mehrere Anweisungskategorien ohne Domänenbeschränkungen, wobei hauptsächlich die Anweisungsverfolgung von LLMs bei allgemeinen Aufgaben verbessert wird. Datensätze werden basierend auf Baumethoden klassifiziert.

Datensatzinformatformat:

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

Menschlich erzeugte Datensätze (HG)

Databricks-Dolly-15K 2023-4 | Alle | En | Hg | Datensatz | Webseite
- Verlag: Databricks
- Größe: 15011 Instanzen
- Lizenz: CC-mal-Sa-3.0
- Quelle: Manuell erzeugt basierend auf verschiedenen Anweisungskategorien
- Anweisungskategorie: Multi
Anweisungenwild_v2 2023-6 | Alle | En & zh | Hg | Github
- Verlag: Nationale Universität von Singapur
- Größe: 110k Instanzen
- Lizenz: -
- Quelle: Im Web gesammelt
- Anweisungskategorie: Multi
LCCC 2020-8 | Alle | Zh | Hg | Papier | Github
- Verlag: Tsinghua University et al.
- Größe: 12m Instanzen
- Lizenz: MIT
- Quelle: Crawl -Benutzerinteraktionen in sozialen Medien kriechen
- Anweisungskategorie: Multi
OASST1 2023-4 | Alle | Multi (35) | Hg | Papier | Github | Datensatz
- Verlag: OpenSthsistant
- Größe: 161443 Instanzen
- Lizenz: Apache-2.0
- Quelle: Erzeugt und kommentiert von Menschen
- Anweisungskategorie: Multi
OL-CC 2023-6 | Alle | Zh | Hg | Datensatz
- Verlag: Baai
- Größe: 11655 Instanzen
- Lizenz: Apache-2.0
- Quelle: Erzeugt und kommentiert von Menschen
- Anweisungskategorie: Multi
Zhihu-kol 2023-3 | Alle | Zh | Hg | Github | Datensatz
- Verlag: Wangrui6
- Größe: 1006218 Instanzen
- Lizenz: MIT
- Quelle: kriechen von Zhihu
- Anweisungskategorie: Multi
AYA-Datensatz 2024-2 | Alle | Multi (65) | Hg | Papier | Datensatz | Webseite
- Verlag: Coher für AI Community et al.
- Größe: 204K -Instanzen
- Lizenz: Apache-2.0
- Quelle: Manuell gesammelt und über die AYA -Annotationsplattform kommentiert
- Anweisungskategorie: Multi
Instructie 2023-5 | Alle | En & zh | Hg | Papier | Github | Datensatz
- Verlag: Zhejiang University et al.
- Größe: 371700 Instanzen
- Lizenz: MIT
- Quelle: Baidu Baike, Wikipedia
- Anweisungskategorie: Extraktion

Modell konstruierte Datensätze (MC)

ALPACA_DATA 2023-3 | Alle | En | MC | Github
- Verlag: Stanford Alpaka
- Größe: 52K -Instanzen
- Lizenz: Apache-2.0
- Quelle: Erzeugt durch Text-Davinci-003 mit aplaca_data-Eingaben
- Anweisungskategorie: Multi
Belle_generated_chat 2023-5 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 396004 Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt von Chatgpt
- Anweisungskategorie: Generation
Belle_multiturn_chat 2023-5 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 831036 Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt von Chatgpt
- Anweisungskategorie: Multi
Belle_train_0.5m_cn 2023-4 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 519255 Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt durch Text-Davinci-003
- Anweisungskategorie: Multi
Belle_train_1m_cn 2023-4 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 917424 Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt durch Text-Davinci-003
- Anweisungskategorie: Multi
Belle_train_2m_cn 2023-5 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 2m Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt von Chatgpt
- Anweisungskategorie: Multi
Belle_train_3.5m_cn 2023-5 | Alle | Zh | MC | Github | Datensatz
- Verlag: Belle
- Größe: 3606402 Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt von Chatgpt
- Anweisungskategorie: Multi
Kamel 2023-3 | Alle | Multi & PL | MC | Papier | Github | Datensatz | Webseite
- Verlag: Kaust
- Größe: 1659328 Instanzen
- Lizenz: CC-mal-NC-4.0
- Quelle: Dialog, der von zwei GPT-3,5-Turbo-Agenten erzeugt wird
- Anweisungskategorie: Multi
CHATGPT_CORPUS 2023-6 | Alle | Zh | MC | Github
- Verlag: Plexpt
- Größe: 3270k Instanzen
- Lizenz: GPL-3.0
- Quelle: Erzeugt durch GPT-3,5-Turbo
- Anweisungskategorie: Multi
Anweisungenwild_v1 2023-3 | Alle | En & zh | MC | Github
- Verlag: Nationale Universität von Singapur
- Größe: 104K -Instanzen
- Lizenz: -
- Quelle: Erzeugt durch OpenAI -API
- Anweisungskategorie: Multi
LMSYS-CHAT-1M 2023-9 | Alle | Multi | MC | Papier | Datensatz
- Verlag: UC Berkeley et al.
- Größe: 1m Instanzen
- Lizenz: LMSYS-CHAT-1M Lizenz
- Quelle: Erzeugt durch mehrere LLMs
- Anweisungskategorie: Multi
MOSS_002_SFT_DATA 2023-4 | Alle | En & zh | MC | Github | Datensatz
- Verlag: Fudan University
- Größe: 1161137 Instanzen
- Lizenz: CC-mal-NC-4.0
- Quelle: Erzeugt durch Text-Davinci-003
- Anweisungskategorie: Multi
MOSS_003_SFT_DATA 2023-4 | Alle | En & zh | MC | Github | Datensatz
- Verlag: Fudan University
- Größe: 1074551 Instanzen
- Lizenz: CC-mal-NC-4.0
- Quelle: Konversationsdaten von MOSS-002 und von GPT-3,5-Turbo generiert
- Anweisungskategorie: Multi
MOSS_003_SFT_PLUGIN_DATA 2023-4 | Partiell | En & zh | MC | Github | Datensatz
- Verlag: Fudan University
- Größe: 300K -Instanzen
- Lizenz: CC-mal-NC-4.0
- Quelle: Erzeugt von Plugins und LLMs
- Anweisungskategorie: Multi
OpenChat 2023-7 | Alle | En | MC | Papier | Github | Datensatz
- Verlag: Tsinghua University et al.
- Größe: 70k Instanzen
- Lizenz: MIT
- Quelle: Sharegpt
- Anweisungskategorie: Multi
Redgpt-Datenet-V1-CN 2023-4 | Partiell | Zh | MC | Github
- Herausgeber: Da-Southampton
- Größe: 50k Instanzen
- Lizenz: Apache-2.0
- Quelle: von LLMs erzeugt
- Anweisungskategorie: Multi
Selbststruktur 2022-12 | Alle | En | MC | Papier | Github
- Verlag: University of Washington et al.
- Größe: 52445 Instanzen
- Lizenz: Apache-2.0
- Quelle: Erzeugt durch GPT-3
- Anweisungskategorie: Multi
Sharechat 2023-4 | Alle | Multi | MC | Webseite
- Verlag: Sharechat
- Größe: 90k Instanzen
- Lizenz: CC0
- Quelle: Sharegpt
- Anweisungskategorie: Multi
Sharegpt-chinese-english-90K 2023-7 | Alle | En & zh | MC | Github | Datensatz
- Verlag: Shareai
- Größe: 90k Instanzen
- Lizenz: Apache-2.0
- Quelle: Sharegpt
- Anweisungskategorie: Multi
Sharegpt90K 2023-4 | Alle | En | MC | Datensatz
- Verlag: Ryokoai
- Größe: 90k Instanzen
- Lizenz: CC0
- Quelle: Sharegpt
- Anweisungskategorie: Multi
Ultrachat 2023-5 | Alle | En | MC | Papier | Github
- Verlag: Tsinghua University
- Größe: 1468352 Instanzen
- Lizenz: CC-mal-NC-4.0
- Quelle: Dialog, der von zwei Chatgpt -Agenten erzeugt wird
- Anweisungskategorie: Multi
Unnatürliche Anweisungen 2022-12 | Alle | En | MC | Papier | Github
- Verlag: Tel Aviv University et al.
- Größe: 240670 Instanzen
- Lizenz: MIT
- Quelle: von LLMs erzeugt
- Anweisungskategorie: Multi
WebGLM-QA 2023-6 | Alle | En | MC | Papier | Github | Datensatz
- Verlag: Tsinghua University et al.
- Größe: 44979 Instanzen
- Lizenz: Apache-2.0
- Quelle: Construct WebGLM-QA über LLM-In-Kontext-Bootstrapping konstruieren
- Anweisungskategorie: QA Öffnen Sie
Wizard_evol_instruct_196K 2023-6 | Alle | En | MC | Papier | Github | Datensatz
- Verlag: Microsoft et al.
- Größe: 196k Instanzen
- Lizenz: -
- Quelle: Evolve Anweisungen durch die EVOL-Instrukturmethode
- Anweisungskategorie: Multi
Wizard_evol_instruct_70K 2023-5 | Alle | En | MC | Papier | Github | Datensatz
- Verlag: Microsoft et al.
- Größe: 70k Instanzen
- Lizenz: -
- Quelle: Evolve Anweisungen durch die EVOL-Instrukturmethode
- Anweisungskategorie: Multi
Wildchat 2024-5 | Partiell | Multi | MC | Papier | Datensatz
- Verlag: Cornell University et al.
- Größe: 1039785 Instanzen
- Lizenz: AI2 Impact Lizenz
- Quelle: Gespräche zwischen Benutzern und Chatgpt, GPT-4
- Anweisungskategorie: Multi

Sammlung und Verbesserung vorhandener Datensätze (CI)

CrossFit 2021-4 | Alle | En | Ci | Papier | Github
- Verlag: Universität von Südkalifornien
- Größe: 269 Datensätze
- Lizenz: -
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
Dialogstudio 2023-7 | Alle | En | Ci | Papier | Github | Datensatz
- Verlag: Salesforce AI et al.
- Größe: 87 Datensätze
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
Dynosaurier 2023-5 | Alle | En | Ci | Papier | Github | Datensatz | Webseite
- Verlag: UCLA et al.
- Größe: 801900 Instanzen
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
Flan-mini 2023-7 | Alle | En | Ci | Papier | Github | Datensatz
- Verlag: Singapur Universität für Technologie und Design
- Größe: 1,34 m Instanzen
- Lizenz: CC
- Quelle: Sammlung und Verbesserung verschiedener Anweisungen Feinabstimmungsdatensätze
- Anweisungskategorie: Multi
Flan 2021 2021-9 | Alle | Multi | Ci | Papier | Github
- Verlag: Google Research
- Größe: 62 Datensätze
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
Flan 2022 2023-1 | Partiell | Multi | Ci | Papier | Github | Datensatz
- Verlag: Google Research
- Größe: 1836 Datensätze
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener Anweisungen Feinabstimmungsdatensätze
- Anweisungskategorie: Multi
Unterweisen 2022-5 | Alle | En | Ci | Papier | Github
- Verlag: Carnegie Mellon University
- Größe: 59 Datensätze
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
Natürliche Anweisungen 2021-4 | Alle | En | Ci | Papier | Github | Datensatz
- Verlag: Allen Institute für AI et al.
- Größe: 61 Datensätze
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener NLP -Datensätze
- Anweisungskategorie: Multi
OIG 2023-3 | Alle | En | Ci | Datensatz
- Verlag: Laion
- Größe: 3878622 Instanzen
- Lizenz: Apache-2.0
- Quelle: Sammlung und Verbesserung verschiedener Datensätze
- Anweisungskategorie: Multi
Open-Platypus 2023-8 | Alle | En | Ci | Papier | Github | Datensatz | Webseite
- Verlag: Boston University
- Größe: 24926 Instanzen
- Lizenz: -
- Source: Collection and improvement of various datasets
- Instruction Category: Multi
OPT-IML Bench 2022-12 | Not | Multi | CI | Paper | Github
- Publisher: Meta AI
- Size: 2000 datasets
- License: MIT
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
PromptSource 2022-2 | All | EN | CI | Paper | Github
- Publisher: Brown University et al.
- Size: 176 datasets
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
SUPER-NATURAL INSTRUCTIONS 2022-4 | All | Multi | CI | Paper | Github
- Publisher: Univ. of Washington et al.
- Size: 1616 datasets
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
T0 2021-10 | All | EN | CI | Paper | Dataset1 | Dataset2
- Publisher: Hugging Face et al.
- Size: 62 datasets
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
UnifiedSKG 2022-3 | All | EN | CI | Paper | Github
- Publisher: The University of Hong Kong et al.
- Size: 21 datasets
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
xP3 2022-11 | All | Multi (46) | CI | Paper | Github
- Publisher: Hugging Face et al.
- Size: 82 datasets
- License: Apache-2.0
- Source: Collection and improvement of various NLP datasets
- Instruction Category: Multi
IEPile 2024-2 | All | EN & ZH | CI | Paper | Github | Datensatz
- Publisher: Zhejiang University et al.
- Size: 33 datasets
- License: CC-BY-NC-SA-4.0
- Source: Collection and improvement of various IE datasets
- Instruction Category: Extraction
KOLLM-Conversations 2024-3 | All | KO | CI | Datensatz
- Publisher: davidkim205
- Size: 1122566 instances
- License: Apache-2.0
- Source: Collection and improvement of Korean datasets
- Instruction Category: Multi

HG & CI

Firefly 2023-4 | All | ZH | HG & CI | Github | Datensatz
- Publisher: YeungNLP
- Size: 1649399 instances
- License: -
- Source: Collect Chinese NLP datasets and manually generate data related to Chinese culture
- Instruction Category: Multi
LIMA-sft 2023-5 | All | EN | HG & CI | Paper | Datensatz
- Publisher: Meta AI et al.
- Size: 1330 instances
- License: CC-BY-NC-SA
- Source: Manually select from various types of data
- Instruction Category: Multi
COIG-CQIA 2024-3 | All | ZH | HG & CI | Paper | Datensatz
- Publisher: Shenzhen Institute of Advanced Technology et al.
- Size: 48375 instances
- License: -
- Source: Q&A communities, Wikipedia, examinations, existing NLP datasets
- Instruction Category: Multi

HG & MC

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

CI & MC

Alpaca_GPT4_data 2023-4 | All | EN | CI & MC | Paper | Github
- Publisher: Microsoft Research
- Size: 52K instances
- License: Apache-2.0
- Source: Generated by GPT-4 with Aplaca_data prompts
- Instruction Category: Multi
Alpaca_GPT4_data_zh 2023-4 | All | ZH | CI & MC | Github | Datensatz
- Publisher: Microsoft Research
- Size: 52K instances
- License: Apache-2.0
- Source: Generated by GPT-4 with Alpaca_data prompts translated into Chinese by ChatGPT
- Instruction Category: Multi
Bactrain-X 2023-5 | All | Multi (52) | CI & MC | Paper | Github | Datensatz
- 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 | Datensatz
- Publisher: University of California et al.
- Size: 210311 instances
- License: GPL-3.0
- Source: Sample seeds from specific datasets to create multi-turn dialogues using ChatGPT
- Instruction Category: Multi
GPT4All 2023-3 | All | EN | CI & MC | Paper | Github | Datensatz
- Publisher: nomic-ai
- Size: 739259 instances
- License: MIT
- Source: Generated by GPT-3.5-Turbo with other datasets' prompts
- Instruction Category: Multi
GuanacoDataset 2023-3 | All | Multi | CI & MC | Dataset | Webseite
- Publisher: JosephusCheung
- Size: 534530 instances
- License: GPL-3.0
- Source: Expand upon the initial 52K dataset from the Alpaca model
- Instruction Category: Multi
LaMini-LM 2023-4 | All | EN | CI & MC | Paper | Github | Datensatz
- Publisher: Monash University et al.
- Size: 2585615 instances
- License: CC-BY-NC-4.0
- Source: Generated by ChatGPT with synthetic and existing prompts
- Instruction Category: Multi
LogiCoT 2023-5 | All | EN & ZH | CI & MC | Paper | Github | Datensatz
- Publisher: Westlake University et al.
- Size: 604840 instances
- License: CC-BY-NC-ND-4.0
- Source: Expand the datasets using GPT-4
- Instruction Category: Reasoning
LongForm 2023-4 | All | EN | CI & MC | Paper | Github | Datensatz
- Publisher: LMU Munich et al.
- Size: 27739 instances
- License: MIT
- Source: Select documents from existing corpora and generating prompts for the documents using LLMs
- Instruction Category: Multi
Luotuo-QA-B 2023-5 | All | EN & ZH | CI & MC | Github | Datensatz
- Publisher: Luotuo
- Size: 157320 instances
- License: Apache-2.0 & CC0
- Source: Use LLMs to generate Q&A pairs on CSL, arXiv, and CNN-DM datasets
- Instruction Category: Multi
OpenOrca 2023-6 | All | Multi | CI & MC | Paper | Datensatz
- Publisher: Microsoft Researc
- Size: 4233923 instances
- License: MIT
- Source: Expand upon the Flan 2022 dataset using GPT-3.5-Turbo and GPT-4
- Instruction Category: Multi
Wizard_evol_instruct_zh 2023-5 | All | ZH | CI & MC | Github | Datensatz
- Publisher: Central China Normal University et al.
- Size: 70K instances
- License: CC-BY-4.0
- Source: Generated by GPT with Wizard_evol_instruct prompts translated into Chinese
- Instruction Category: Multi
Lithuanian-QA-v1 2024-8 | All | LT | CI & MC | Paper | Datensatz
- Publisher: Neurotechnology
- Size: 13848 instances
- License: CC-BY-4.0
- Source: Use ChatGPT to generate Q&A pairs on Wikipedia corpus
- Instruction Category: Multi
LongWriter-6K 2024-8 | All | EN & ZH | CI & MC | Paper | Github | Datensatz
- Publisher: Tsinghua University et al.
- Size: 6000 instances
- License: Apache-2.0
- Source: Generated by GPT-4o with open-source datasets' prompts
- Instruction Category: Multi

HG & CI & MC

COIG 2023-4 | All | ZH | HG & CI & MC | Paper | Github | Datensatz
- Publisher: BAAI
- Size: 191191 instances
- License: Apache-2.0
- Source: Translated instructions, Leetcode, Chinese exams, etc.
- Instruction Category: Multi
HC3 2023-1 | All | EN & ZH | HG & CI & MC | Paper | Github | Dataset1 | Dataset2
- Publisher: SimpleAI
- Size: 37175 instances
- License: CC-BY-SA-4.0
- Source: Human-Q&A pairs and ChatGPT-Q&A pairs from Q&A platforms, encyclopedias, etc.
- Instruction Category: Multi
Phoenix-sft-data-v1 2023-5 | All | Multi | HG & CI & MC | Paper | Github | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- Publisher: TigerBot
- Size: 530705 instances
- License: Apache-2.0
- Source: Self-instruct, human-labeling, open-source data cleaning
- Instruction Category: Multi
Aya Collection 2024-2 | All | Multi (114) | HG & CI & MC | Paper | Dataset | Webseite
- 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:

Medizinisch

ChatDoctor 2023-3 | All | EN | HG & MC | Paper | Github | Datensatz
- Publisher: University of Texas Southwestern Medical Center et al.
- Size: 115K instances
- License: Apache-2.0
- Source: Real conversations between doctors and patients & Generated by ChatGPT
- Instruction Category: Multi
- Domain: Medical
ChatMed_Consult_Dataset 2023-5 | All | ZH | MC | Github | Datensatz
- Publisher: michael-wzhu
- Size: 549326 instances
- License: CC-BY-NC-4.0
- Source: Generated by GPT-3.5-Turbo
- Instruction Category: Multi
- Domain: Medical
CMtMedQA 2023-8 | All | ZH | HG | Paper | Github | Datensatz
- 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 | Webseite
- Publisher: Fudan University et al.
- Size: 464898 instances
- License: Apache-2.0
- Source: Open source datasets & Manually selected data
- Instruction Category: Multi
- Domain: Medical
HuatuoGPT-sft-data-v1 2023-5 | All | ZH | HG & MC | Paper | Github | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- Publisher: michael-wzhu
- Size: 112565 instances
- License: Apache-2.0
- Source: Generated by ChatGPT
- Instruction Category: Multi
- Domain: Medical

Code

Code_Alpaca_20K 2023-3 | All | EN & PL | MC | Github | Datensatz
- 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 | Datensatz
- 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

Legal

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

Mathe

BELLE_School_Math 2023-5 | All | ZH | MC | Github | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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

Ausbildung

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 | Datensatz
- 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 | Datensatz
- 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

Andere

DISC-Fin-SFT 2023-10 | Partial | ZH | HG & CI & MC | Paper | Github | Webseite
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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:

Abstimmung

Chatbot_arena_conversations 2023-6 | All | Multi | HG & MC | Paper | Datensatz
- 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 | Datensatz
- 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 | Webseite
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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

Sortieren

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

Punktzahl

Stack-Exchange-Preferences 2021-12 | All | EN | HG | Paper | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- 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

Andere

Medical-rlhf 2023-5 | All | ZH | CI & MC | Github | Datensatz
- 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:

Allgemein

AlpacaEval 2023-5 | All | EN | CI & MC | Paper | Github | Dataset | Webseite
- 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 | Datensatz
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Datensatz
- 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

Prüfung

AGIEval 2023-4 | All | EN & ZH | HG & CI | Paper | Github | Datensatz
- 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

Thema

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 | Webseite
- Publisher: Tianjin University
- Größe: -
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Datensatz
- 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 | Webseite
- 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 | Datensatz
- Publisher: University of Waterloo et al.
- Size: 800 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: Science subject question-answering ability
- Numbers of Evaluation Categories/Subcategories: 4/39
- Evaluation Category: Mathematics, Physics, Finance, CS & EE
XiezhiBenchmark 2023-6 | All | EN & ZH | HG & MC | Paper | Github
- Publisher: Fudan University et al.
- Size: 249587 instances
- License: CC-BY-NC-SA-4.0
- Question Type: OQ
- Evaluation Method: CE
- Focus: Multidisciplinary abilities
- Numbers of Evaluation Categories/Subcategories: 13/516
- Evaluation Category: Medicine, Literature, Economics, Agronomy, Science, Jurisprudence, History, Art studies, Philosophy, Pedagogy, Military science, Management, Engineering
CMMLU 2023-6 | All | ZH | HG | Paper | Github | Datensatz
- 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 | Datensatz
- 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 | Datensatz
- Publisher: Xiaomi AI Lab
- Size: 1698 instances
- License: CC-BY-4.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: Elementary school math word problems
- Numbers of Evaluation Categories/Subcategories: 6/-
- Evaluation Category: Grades 1 to 6 in elementary school
FineMath 2024-3 | Not | ZH | HG | Papier
- Publisher: Tianjin University et al.
- Size: 1584 instances
- License: -
- Question Type: Multi
- Evaluation Method: -
- Focus: Elementary school math word problems
- Numbers of Evaluation Categories/Subcategories: 6/17
- Evaluation Category: Number & Operations, Measurement, Data analysis & Probability, Algebra, Geometry, Others
WYWEB 2023-7 | All | ZH | HG & CI | Paper | Github | Dataset | Webseite
- 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 | Datensatz
- 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 | Datensatz
- Publisher: Zhejiang University et al.
- Size: 50048 instances
- License: -
- Question Type: Multi
- Evaluation Method: CE & ME
- Focus: Evaluate the capabilities of LLMs in handling scientific knowledge
- Numbers of Evaluation Categories/Subcategories: 2/49
- Evaluation Category: Biology, Chemistry
C ³ Bench 2024-5 | All | ZH | HG & CI | Papier
- Publisher: South China University of Technology
- Size: 50000 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Classical Chinese
- Numbers of Evaluation Categories/Subcategories: 5/-
- Evaluation Category: Classification, Retrieval, NER, Punctuation, Translation
ArabicMMLU 2024-8 | All | AR | HG | Paper | Github | Dataset
- Publisher: MBZUAI et al.
- Size: 14575 instances
- License: CC-BY-NC-SA-4.0
- Question Type: OQ
- Evaluation Method: CE
- Focus: Multi-task language understanding benchmark for the Arabic language
- Numbers of Evaluation Categories/Subcategories: 5/40
- Evaluation Category: STEM, Social science, Humanities, Language, Other
PersianMMLU 2024-4 | All | FA | HG | Paper | Dataset
- Publisher: Raia Center for Artificial Intelligence Research et al.
- Size: 20192 instances
- License: CC-ND
- Question Type: OQ
- Evaluation Method: CE
- Focus: Facilitate the rigorous evaluation of LLMs that support the Persian language
- Numbers of Evaluation Categories/Subcategories: 5/38
- Evaluation Category: Social science, Humanities, Natural science, Mathematics, Other
TMMLU+ 2024-3 | All | ZH | HG & CI | Paper | Datensatz
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Ought et al.
- Size: 28712 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Text classification ability
- Numbers of Evaluation Categories/Subcategories: 1/11
- Evaluation Category: Text classification
SentEval 2018-5 | All | EN | CI | Paper | Github
- Publisher: Facebook Artificial Intelligence Research
- Size: 28 datasets
- License: BSD
- Question Type: SQ
- Evaluation Method: CE
- Focus: The quality of universal sentence representations
- Numbers of Evaluation Categories/Subcategories: 1/21
- Evaluation Category: Universal sentence representations
LeSC 2024-5 | All | EN & ZH | HG | Paper | Github | Dataset
- Publisher: Tsinghua University et al.
- Size: 600 instances
- License: MIT
- Question Type: OQ
- Evaluation Method: CE
- Focus: The genuine linguistic-cognitive skills of LLMs
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Polysemy
KoBEST 2022-10 | All | KO | CI | Paper | Dataset
- Publisher: University of Oxford et al.
- Size: 5 datasets
- License: CC-BY-SA-4.0
- Question Type: OQ
- Evaluation Method: CE
- Focus: Korean balanced evaluation of significant tasks
- Numbers of Evaluation Categories/Subcategories: 5/-
- Evaluation Category: KB-BoolQ, KB-COPA, KB-WiC, KB-HellaSwag, KB-SentiNeg
SarcasmBench 2024-8 | All | EN | CI | Papier
- Publisher: Tianjin University et al.
- Size: 58347 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Evaluate LLMs on sarcasm understanding
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Sarcasm understanding

Argumentation

Chain-of-Thought Hub 2023-5 | All | EN | CI | Paper | Github
- Publisher: University of Edinburgh et al.
- Größe: -
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: The multi-step reasoning capabilities
- Numbers of Evaluation Categories/Subcategories: 6/8
- Evaluation Category: Math, Science, Symbolic, Knowledge, Coding, Factual
Choice-75 2023-9 | All | EN | HG & CI & MC | Paper | Github
- Publisher: University of Pittsburgh et al.
- Size: 650 instances
- License: -
- Question Type: OQ
- Evaluation Method: CE
- Focus: Predict decisions based on descriptive scenarios
- Numbers of Evaluation Categories/Subcategories: 4/-
- Evaluation Category: Easy, Medium, Hard, N/A
NeuLR 2023-6 | All | EN | CI | Paper | Github | Dataset
- Publisher: Xi'an Jiaotong University et al.
- Size: 3000 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: Logical reasoning capabilities
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: Deductive, Inductive, Abductive
TabMWP 2022-9 | All | EN | HG | Paper | Github | Webseite
- Publisher: University of California et al.
- Size: 38431 instances
- License: CC-BY-NC-SA-4.0
- Question Type: Multi
- Evaluation Method: CE
- Focus: Mathematical reasoning ability involving both textual and tabular information
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Mathematical reasoning and table QA
LILA 2022-10 | All | EN | CI | Paper | Github | Dataset
- Publisher: Arizona State Univeristy et al.
- Size: 317262 instances
- License: CC-BY-4.0
- Question Type: Multi
- Evaluation Method: CE
- Focus: Mathematical reasoning across diverse tasks
- Numbers of Evaluation Categories/Subcategories: 4/23
- Evaluation Category: Math ability, Language, Knowledge, Format
MiniF2F_v1 2021-9 | All | EN | HG & CI | Paper | Github
- Publisher: Ecole Polytechnique et al.
- Size: 488 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: The performance on formal Olympiad-level mathematics problem statements
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Math
GameBench 2024-6 | All | EN | HG | Paper | Github | Dataset
- Publisher: Olin College of Engineering et al.
- Size: 9 Games
- License: CC-BY
- Question Type: SQ
- Evaluation Method: CE
- Focus: Evaluate strategic reasoning abilities of LLM agents
- Numbers of Evaluation Categories/Subcategories: 6/9
- Evaluation Category: Abstract Strategy, Non-Deterministic, Hidden Information, Language Communication, Social Deduction, Cooperation
TableBench 2024-8 | All | EN | HG & CI & MC | Paper | Github | Dataset | Webseite
- 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

Wissen

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 | Webseite
- 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 | Webseite
- Publisher: Tsinghua University et al.
- Size: 10090 instances
- License: -
- Question Type: SQ
- Evaluation Method: ME
- Focus: The performance on open-ended question answering
- Numbers of Evaluation Categories/Subcategories: 3/25
- Evaluation Category: Knowledge memorization, Knowledge comprehension, Knowledge analysis
DebateQA 2024-8 | All | EN | HG & CI & MC | Paper | Github | Dataset
- Publisher: Tsinghua Universty et al.
- Size: 2941 instances
- License: -
- Question Type: SQ
- Evaluation Method: ME
- Focus: Evaluate the comprehensiveness of perspectives and assess whether the LLM acknowledges the question's debatable nature
- Numbers of Evaluation Categories/Subcategories: 2/-
- Evaluation Category: Perspective diversity, Dispute awareness

Long Text

L-Eval 2023-7 | All | EN | HG & CI | Paper | Github | Dataset
- Publisher: Fudan University et al.
- Size: 2043 instances
- License: GPL-3.0
- Question Type: SQ
- Evaluation Method: HE & CE & ME
- Focus: Long text task capability
- Numbers of Evaluation Categories/Subcategories: 1/18
- Evaluation Category: Long text task
LongBench 2023-8 | All | EN & ZH | CI | Paper | Github | Dataset
- Publisher: Tsinghua University et al.
- Size: 4750 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: Long text task capability
- Numbers of Evaluation Categories/Subcategories: 6/21
- Evaluation Category: Single-doc QA, Multi-doc QA, Summarization, Few-shot learning, Synthetic tasks, Code completion
LongEval 2023-6 | All | EN | HG | Github | Webseite
- Publisher: LMSYS
- Größe: -
- 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 | Webseite
- 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.
- Größe: -
- 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
- Größe: -
- 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.
- Größe: -
- 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

Werkzeug

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

Agent

AgentBench 2023-8 | All | EN | HG & CI & MC | Paper | Github | Webseite
- 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
- Größe: -
- License: -
- Question Type: SQ
- Evaluation Method: -
- Focus: Agent capabilities of LLMs
- Numbers of Evaluation Categories/Subcategories: 3/10
- Evaluation Category: Tool utilization, Task planning, Long-term and short-term memory

Code

BIRD 2023-5 | All | EN & PL | HG & CI & MC | Paper | Github | Dataset | Webseite
- 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 | Datensatz
- 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 | Webseite
- 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 | Webseite
- 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.
- Größe: -
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: Assess model performance under distribution shifts
- Numbers of Evaluation Categories/Subcategories: 5/20
- Evaluation Category: Sentiment analysis, Toxicity detection, Natural language inference, Named entity recognition, Extractive Question answering
GLUE-X 2022-11 | All | EN | CI | Paper | Github | Dataset
- Publisher: Westlake University et al.
- Size: 6404940 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: The out-of-distribution (OOD) robustness
- Numbers of Evaluation Categories/Subcategories: 7/16
- Evaluation Category: Sentiment analysis, Linguistic acceptability, Textual similarity, Natural language inference, Question answering NLI, Textual entailment, Paraphrase

Gesetz

LAiW 2023-10 | Partial | ZH | CI | Paper | Github
- Publisher: Sichuan University et al.
- Größe: -
- 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.
- Größe: -
- 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 | Webseite
- 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/-

Medizinisch

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 | Webseite
- Publisher: The Chinese University of Hong Kong et al.
- Size: 281047 instances
- License: Apache-2.0
- Question Type: Multi
- Evaluation Method: HE & CE & ME
- Focus: The performance of LLMs in the field of medicine
- Numbers of Evaluation Categories/Subcategories: 2/7
- Evaluation Category: CMB-Exam, CMB-Clin
HuaTuo26M-test 2023-5 | All | ZH | CI | Paper | Github | Dataset
- Publisher: The Chinese University of Hong Kong et al.
- Size: 6000 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: Understand and generate complex medical language
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: Medical consultant records, Encyclopedias, Knowledge bases
MultiMedQA 2022-12 | All | EN | HG & CI | Paper | Dataset
- Publisher: Google Research et al.
- Size: 212822 instances
- License: -
- Question Type: Multi
- Evaluation Method: HE & CE
- Focus: The performance in medical and clinical applications
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Medical question answering
PromptCBLUE 2023-4 | All | ZH | CI | Github
- Publisher: East China Normal University et al.
- Size: 20640 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: The performance in Chinese medical scenarios
- Numbers of Evaluation Categories/Subcategories: 16/-
- Evaluation Category: Medical named entity recognition, Medical entity relation extraction, Medical event extraction, etc.
QiZhenGPT_eval 2023-5 | All | ZH | HG | Github | Dataset
- Publisher: Zhejiang University et al.
- Size: 94 instances
- License: GPL-3.0
- Question Type: SQ
- Evaluation Method: HE
- Focus: Indications for use of drugs
- Numbers of Evaluation Categories/Subcategories: 1/-
- Evaluation Category: Drug indication question answering
CLUE 2024-4 | Partical | EN | HG & CI & MC | Paper | Github
- Publisher: University Hospital Essen et al.
- Größe: -
- 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

Finanziell

BBF-CFLEB 2023-2 | All | ZH | HG & CI | Paper | Github | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: CLUEbenchmark
- Size: 4912 instances
- License: -
- Question Type: SQ
- Evaluation Method: ME
- Focus: The safety of LLMs
- Numbers of Evaluation Categories/Subcategories: 3/20+
- Evaluation Category: Traditional security category, Responsible artificial intelligence, Instruction attacks
TRUSTGPT 2023-6 | All | EN | CI | Paper | Github
- Publisher: Sichuan University et al.
- Size: 2000 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: The performance in toxicity, bias, and value alignment
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: Toxicity, Bias, Value-alignment

Factuality

FACTOR 2023-7 | Partial | EN | HG & CI & MC | Paper | Github
- Publisher: AI21 Labs
- Size: 4030 instances
- License: MIT
- Question Type: OQ
- Evaluation Method: CE
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 2/-
- Evaluation Category: Wiki, News
FActScore 2023-5 | All | EN | HG & MC | Paper | Github
- Publisher: University of Washington et al.
- Size: 500 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: HE & ME
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 7/-
- Evaluation Category: Single-sentence contradiction (words or beyond words), Page-level contradiction, Subjective, Fact is irrelevant, Wiki is inconsistent & wrong, Annotation error
FactualityPrompt 2022-6 | All | EN | CI | Paper | Github
- Publisher: Hong Kong University of Science and Technology et al.
- Size: 16000 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 2/-
- Evaluation Category: Factual prompts, Nonfactual prompts
FreshQA 2023-10 | All | EN | HG | Paper | Github
- Publisher: Google et al.
- Size: 600 instances
- License: -
- Question Type: SQ
- Evaluation Method: HE
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 4/-
- Evaluation Category: Never-changing, Slow-changing, Fast-changing, False-premise
HalluQA 2023-10 | All | ZH | HG & MC | Paper | Github
- Publisher: Fudan University et al.
- Size: 450 instances
- License: -
- Question Type: SQ
- Evaluation Method: ME
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: Misleading, Misleading-hard, Knowledge
HaluEval 2023-5 | All | EN | HG & CI & MC | Paper | Github | Dataset
- Publisher: Renmin University of China et al.
- Size: 35000 instances
- License: MIT
- Question Type: SQ
- Evaluation Method: CE
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 3/-
- Evaluation Category: QA, Dialogue, Summarization
TruthfulQA 2022-5 | All | EN | HG | Paper | Github
- Publisher: University of Oxford et al.
- Size: 817 instances
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: CE & ME
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 38/-
- Evaluation Category: Health, Law, Conspiracies, Fiction, Misconceptions, Paranormal, Economics, Biology, Language, Indexical etc.
UHGEval 2023-11 | All | ZH | HG & MC | Paper | Github | Dataset
- Publisher: Renmin University of China et al.
- Size: 5141 instances
- License: Apache-2.0
- Question Type: Multi
- Evaluation Method: CE
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 3/4
- Evaluation Category: Discriminative, Selective, Generative
HaluEval-Wild 2024-3 | Not | EN | HG & CI & MC | Papier
- Publisher: Carnegie Mellon University
- Size: 500 instances
- License: -
- Question Type: SQ
- Evaluation Method: -
- Focus: The factuality of LLMs
- Numbers of Evaluation Categories/Subcategories: 5/-
- Evaluation Category: Out-of-scope information, Complex reasoning, Inappropriate content, Beyond-modality interaction, Confused / Erroneous queries
RealTime QA 2022-7 | All | EN | HG | Paper | Github | Dataset | Webseite
- Publisher: Toyota Technological Institute et al.
- Größe: -
- 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 | Webseite
- 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

Auswertung

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.
- Größe: -
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Stanford University et al.
- Größe: -
- License: Apache-2.0
- Question Type: SQ
- Evaluation Method: HE & CE
- Focus: Evaluate LLMs on a wide range of scenarios and metrics
- Numbers of Evaluation Categories/Subcategories: 73/-
- Evaluation Category: Question answering, Information retrieval, Sentiment analysis, Toxicity detection, Aspirational scenarios, etc.
LLMEVAL-1 2023-5 | All | ZH | HG | Github
- Publisher: Fudan University et al.
- Size: 453 instances
- License: -
- Question Type: SQ
- Evaluation Method: HE & ME
- Focus: Multidimensional capabilities
- Numbers of Evaluation Categories/Subcategories: 17/-
- Evaluation Category: Fact-based question answering, Reading comprehension, Framework generation, Paragraph rewriting, etc.
LMentry 2023-7 | All | EN | HG | Paper | Github
- Publisher: Tel Aviv University et al.
- Size: 110703 instances
- License: -
- Question Type: SQ
- Evaluation Method: CE
- Focus: The performance on challenging tasks
- Numbers of Evaluation Categories/Subcategories: 25/-
- Evaluation Category: Sentence containing word, Sentence not containing word, Word containing letter, Word not containing letter, etc.
AlignBench 2023-11 | All | ZH | HG & MC | Paper | Github | Dataset
- Publisher: Tsinghua University et al.
- Size: 683 instances
- License: -
- Question Type: Multi
- Evaluation Method: ME
- Focus: Evaluate the alignment of LLMs on Chinese multitasks.
- Numbers of Evaluation Categories/Subcategories: 8/-
- Evaluation Category: Fundamental language ability, Advanced Chinese understanding, Open-ended questions, Writing ability, Logical reasoning, Mathematics, Task-oriented role play,
- Professional knowledge

Mehrsprachig

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 | Webseite
- Publisher: Carnegie Mellon University et al.
- Größe: -
- 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

Andere

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 | Webseite
- 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 | Webseite
- 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.
- Größe: -
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Carnegie Mellon University
- Train/Dev/Test/All Size: 87866/4887/4934/97687
- License: -
C3 2019-4 | ZH | Paper | Github | Webseite
- Publisher: Cornell University et al.
- Train/Dev/Test/All Size: 11869/3816/3892/19577
- License: -
ReClor 2020-2 | EN | Paper | Webseite
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
DREAM 2020-2 | EN | Paper | Github | Webseite
- Publisher: National University of Singapore
- Train/Dev/Test/All Size: 4638/500/1000/6138
- License: -
QuAIL 2020-4 | EN | Paper | Webseite
- 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 | Datensatz
- Publisher: Microsoft Research
- Train/Dev/Test/All Size: 1200/200/600/2000
- License: -

Cloze Test

ChID 2019-6 | ZH | Paper | Github | Dataset
- Publisher: Tsinghua University et al.
- Train/Dev/Test/All Size: 605k/23.2K/83.3K/711.5K
- License: Apache-2.0
LAMBADA 2016-6 | EN | Paper | Dataset | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Johns Hopkins University et al.
- Train/Dev/Test/All Size: 100730/10000/10000/120730
- License: -
QuAC 2018-8 | EN | Paper | Dataset | Webseite
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: 83568/7354/7353/98407
- License: CC-BY-SA-4.0
TyDiQA 2020-3 | Multi (11) | Paper | Github | Dataset
- Publisher: Google Research
- Train/Dev/Test/All Size: 116916/18670/18751/154337
- License: Apache-2.0
CMRC2018 2019-11 | ZH | Paper | Github
- Publisher: Harbin Institute of Technology et al.
- Train/Dev/Test/All Size: 10321/3351/4895/18567
- License: CC-BY-SA-4.0
Adversarial QA 2020-2 | EN | Paper | Github | Dataset
- Publisher: University College London
- Train/Dev/Test/All Size: 30000/3000/3000/36000
- License: MIT
Quoref 2019-8 | EN | Paper | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Stanford University
- Train/Dev/Test/All Size: -/-/-/127K
- License: -
QASPER 2021-5 | EN | Paper | Webseite
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: -/-/-/5049
- License: CC-BY-4.0
DuoRC 2018-7 | EN | Paper | Dataset | Webseite
- 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 | Webseite
- Publisher: AI2
- Train/Dev/Test/All Size: 3370/869/3548/7787
- License: CC-BY-SA
CommonsenseQA 2018-11 | EN | Paper | Github | Dataset | Webseite
- Publisher: Tel-Aviv University et al.
- Train/Dev/Test/All Size: 9797/1225/1225/12247
- License: MIT
OpenBookQA 2018-10 | EN | Paper | Github | Datensatz
- 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 | Webseite
- 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 | Webseite
- Publisher: Universidade da Coruna
- Train/Dev/Test/All Size: 2657/1366/2742/13530
- License: MIT
SciQ 2017-9 | EN | Paper | Dataset | Webseite
- 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 | Webseite
- Publisher: Georgia Institute of Technology et al.
- Train/Dev/Test/All Size: 2118/296/633/3047
- License: Microsoft Research Data License
ECQA 2021-8 | EN | Paper | Github
- Publisher: IIT Delhi et al.
- Train/Dev/Test/All Size: 7598/1090/2194/10882
- License: CDLA-Sharing-1.0
PsyQA 2021-6 | ZH | Paper | Github
- Publisher: The CoAI group et al.
- Train/Dev/Test/All Size: -/-/-/22346
- License: PsyQA User Agreement
WebMedQA 2018-12 | ZH | Paper | Github
- Publisher: Chinese Academy of Sciences et al.
- Train/Dev/Test/All Size: 50610/6337/6337/63284
- License: Apache-2.0
WebQuestions 2013-10 | EN | Paper | Dataset
- Publisher: Stanford University
- Train/Dev/Test/All Size: 3778/-/2032/5810
- License: -

Reasoning QA

The focal point of reasoning QA tasks is the requirement for models to apply abilities such as logical reasoning, multi-step inference, and causal reasoning in answering questions.

STRATEGYQA 2021-1 | EN | Paper | Webseite
- Publisher: Tel Aviv University et al.
- Train/Dev/Test/All Size: 2290/-/490/2780
- License: MIT
COPA 2011-6 | EN | Paper | Webseite
- 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 | Webseite
- Publisher: AI2
- Train/Dev/Test/All Size: 2696/384/784/3864
- License: CC-BY-4.0
WIQA 2019-9 | EN | Paper | Dataset | Webseite
- Publisher: AI2
- Train/Dev/Test/All Size: 29808/6894/3993/40695
- License: -
QASC 2019-10 | EN | Paper | Dataset | Webseite
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: 8134/926/920/9980
- License: CC-BY-4.0
QuaRel 2018-11 | EN | Paper | Webseite
- Publisher: AI2
- Train/Dev/Test/All Size: 1941/278/552/2771
- License: CC-BY-4.0
ROPES 2019-8 | EN | Paper | Dataset | Webseite
- 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 | Datensatz
- 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 | Webseite
- 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: -

Mathe

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 | Datensatz
- 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 | Webseite
- Publisher: University of Washington et al.
- Train/Dev/Test/All Size: 29837/4475/2985/37297
- License: Apache-2.0
AQUA-RAT 2017-7 | EN | Paper | Github | Dataset
- Publisher: DeepMind
- Train/Dev/Test/All Size: 100949/250/250/101499
- License: Apache-2.0
NaturalProofs 2021-4 | EN | Paper | Github
- Publisher: University of Washington et al.
- Train/Dev/Test/All Size: -/-/-/80795
- License: MIT

Coreference Resolution

The core objective of tasks related to coreference resolution is the identification of referential relationships within texts.

WSC 2012-X | EN | Paper | Dataset
- Publisher: University of Toronto et al.
- Train/Dev/Test/All Size: -/-/285/285
- License: CC-BY-4.0
DPR 2012-7 | EN | Paper | Dataset
- Publisher: University of Texas at Dallas
- Train/Dev/Test/All Size: 1322/-/564/1886
- License: -
WinoGrande 2019-7 | EN | Paper | Github | Dataset
- Publisher: AI2 et al.
- Train/Dev/Test/All Size: 63238/1267/1767/66272
- License: CC-BY
WiC 2018-8 | EN | Paper | Webseite
- 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: -

Stimmungsanalyse

The sentiment analysis task, commonly known as emotion classification, seeks to analyze and deduce the emotional inclination of provided texts, commonly categorized as positive, negative, or neutral sentiments.

IMDB 2011-6 | EN | Paper | Dataset
- Publisher: Stanford University
- Train/Dev/Test/All Size: 25000/-/25000/50000
- License: -
Sentiment140 2009-X | EN | Paper | Dataset
- Publisher: Stanford University
- Train/Dev/Test/All Size: 1600000/-/359/1600359
- License: -
SST-2 2013-10 | EN | Paper | Dataset
- Publisher: Stanford University
- Train/Dev/Test/All Size: 67349/872/1821/70042
- License: -
EPRSTMT 2021-7 | ZH | Paper | Github
- Publisher: CLUE team
- Train/Dev/Test/All Size: 32/32/1363/20992
- License: -

Semantic Matching

The task of semantic matching entails evaluating the semantic similarity or degree of correspondence between two sequences of text.

MRPC 2005-X | EN | Papier
- Publisher: Microsoft Research
- Train/Dev/Test/All Size: 4076/-/1725/5801
- License: -
QQP 2018-11 | EN | Paper | Datensatz
- 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 | Webseite
- Publisher: Google Research et al.
- Train/Dev/Test/All Size: 5749/1500/1379/8628
- License: -
AFQMC 2020-12 | ZH | Papier
- Publisher: CLUE team
- Train/Dev/Test/All Size: 34.3K/4.3K/3.9K/42.5K
- License: -
BQ 2018-10 | ZH | Paper | Dataset
- Publisher: Harbin Institute of Technology et al.
- Train/Dev/Test/All Size: 100000/10000/10000/120000
- License: -
LCQMC 2018-8 | ZH | Papier
- Publisher: Harbin Institute of Technology et al.
- Train/Dev/Test/All Size: 238766/8802/12500/260068
- License: CC-BY-4.0
PAWS-X 2019-8 | Multi (6) | Paper | Github
- Publisher: Google Research
- Train/Dev/Test/All Size: 296406/11815/11844/320065
- License: -
BUSTM 2021-7 | ZH | Paper | Github
- Publisher: CLUE team
- Train/Dev/Test/All Size: 32/32/3772/8087
- License: -
DuQM 2021-9 | ZH | Paper | Github1 | Github2
- Publisher: Baidu Inc. et al.
- Train/Dev/Test/All Size: -/-/-/10121
- License: Apache-2.0

Text Generation

The narrow definition of text generation tasks is bound by provided content and specific requirements. It involves utilizing benchmark data, such as descriptive terms and triplets, to generate corresponding textual descriptions.

CommonGen 2019-11 | EN | Paper | Github | Dataset
- Publisher: University of Southern California et al.
- Train/Dev/Test/All Size: 67389/4018/1497/72904
- License: MIT
DART 2020-7 | EN | Paper | Github | Dataset
- Publisher: Yale University et al.
- Train/Dev/Test/All Size: 30526/2768/6959/40253
- License: MIT
E2E 2017-6 | EN | Paper | Github | Dataset
- Publisher: Heriot-Watt University
- Train/Dev/Test/All Size: 42061/4672/4693/51426
- License: CC-BY-SA-3.0
WebNLG 2017-7 | EN & RU | Paper | Github | Dataset
- Publisher: LORIA et al.
- Train/Dev/Test/All Size: 49665/6490/7930/64085
- License: CC-BY-NC-SA-4.0

Text Translation

Text translation involves transforming text from one language to another.

WMT - | Multi | Webseite
- 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 | Webseite
- 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 | Datensatz
- Publisher: Cornell University
- Train/Dev/Test/All Size: 14732/818/819/16369
- License: CC-BY-NC-ND-4.0
XSum 2018-10 | EN | Paper | Github | Dataset
- Publisher: University of Edinburgh
- Train/Dev/Test/All Size: 204045/11332/11334/226711
- License: MIT
Opinion Abstracts 2016-6 | EN | Paper | Dataset
- Publisher: Northeastern University et al.
- Train/Dev/Test/All Size: 5990/-/-/5990
- License: -
WikiLingua 2020-10 | Multi (18) | Paper | Github | Dataset
- Publisher: Columbia University et al.
- Train/Dev/Test/All Size: -/-/-/770087
- License: CC-BY-3.0
LCSTS 2015-6 | ZH | Paper | Dataset
- Publisher: Harbin Institute of Technology
- Train/Dev/Test/All Size: 2400000/10000/1000/2411000
- License: CC-BY-4.0
CNewSum 2021-10 | ZH | Paper | Github | Dataset | Webseite
- 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 | Webseite
- Publisher: New York University
- Train/Dev/Test/All Size: 120000/-/7600/127600
- License: -
TNEWS 2020-11 | ZH | Paper | Github | Dataset
- Publisher: CLUE team
- Train/Dev/Test/All Size: 53.3K/10K/10K/73.3K
- License: -
IFLYTEK 2020-12 | ZH | Papier
- Publisher: CLUE team
- Train/Dev/Test/All Size: 12.1K/2.6K/2.6K/17.3K
- License: -
MARC 2020-11 | Multi (6) | Paper | Dataset
- Publisher: Amazon et al.
- Train/Dev/Test/All Size: 1200000/30000/30000/1260000
- License: -
THUCNews 2016-X | ZH | Github | Webseite
- Publisher: Tsinghua University
- Train/Dev/Test/All Size: -/-/-/1672165
- License: MIT
CSLDCP 2021-7 | ZH | Paper | Github | Webseite
- 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 | Webseite
- 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 | Webseite
- Publisher: Westlake University
- Train/Dev/Test/All Size: -/-/-/10181
- License: CC-BY-SA-4.0
Spider 2018-9 | EN & PL | Paper | Github | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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 | Webseite
- 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

Unterlagen

mOSCAR : A large-scale multilingual and multimodal document-level corpus
- Paper: mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
- Github: https://oscar-project.github.io/documentation/versions/mOSCAR/
OBELISC : An open web-scale filtered dataset of interleaved image-text documents
- Paper: OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
- Github: https://github.com/huggingface/OBELICS
- Dataset: https://huggingface.co/datasets/HuggingFaceM4/OBELICS

Instruction Fine-tuning Datasets

Remote Sensing

MMRS-1M : Multi-sensor remote sensing instruction dataset
- Paper: EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
- Github: https://github.com/wivizhang/EarthGPT

Images + Videos

VideoChat2-IT : Instruction fine-tuning dataset for images/videos
- Paper: MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
- Dataset: https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT

Visual Document Understanding

InstructDoc : A dataset for zero-shot generalization of visual document understanding
- Paper: InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions
- Github: https://github.com/nttmdlab-nlp/InstructDoc
- Dataset: https://github.com/nttmdlab-nlp/InstructDoc

Allgemein

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

Thema

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

Medizinisch

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

Kontakt

Kontaktinformationen:

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!

Zitat

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}
}

Expandieren