mlsanityData to 7 Oct 2026

FEVER: a large-scale dataset for Fact Extraction and VERification

arXiv 1803.05355 ↗First posted 14 Mar 2018cs.CLcited by 1,362

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Cited by 157 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87%, while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.

Key facts. Cited by 1,362 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2018.

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Citing papers per year: 2020: 1; 2021: 138; 2022: 148; 2023: 213; 2024: 284; 2025: 273; 2026: 305.

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  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
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  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
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  7. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  8. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
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  10. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  11. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  12. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
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  5. 2023A Comprehensive Overview of Large Language Modelscited by 639
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  10. 2021Fast Model Editing at Scalecited by 491
  11. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 466
  12. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
  13. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  14. 2021MetaICL: Learning to Learn In Contextcited by 450
  15. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  16. 2022Memory-Based Model Editing at Scalecited by 443
  17. 2024Understanding the planning of LLM agents: A surveycited by 414
  18. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  19. 2024LLM2Vec: Large Language Models Are Secretly Powerful Text Encoderscited by 396
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  27. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  28. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
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  34. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
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  38. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253

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