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EMNLP Findings2022

SciFact-Open: Towards open-domain scientific claim verification

David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Iz Beltagy, Lucy Lu Wang, Hannaneh Hajishirzi

摘要

While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic setting against large corpora of scientific literature. Moving to this open-domain evaluation setting, however, poses unique challenges; in particular, it is infeasible to exhaustively annotate all evidence documents. In this work, we present SCIFACT-OPEN, a new test collection designed to evaluate the performance of scientific claim verification systems on a corpus of 500K research abstracts. Drawing upon pooling techniques from information retrieval, we collect evidence for scientific claims by pooling and annotating the top predictions of four state-of-the-art scientific claim verification models. We find that systems developed on smaller corpora struggle to generalize to SCIFACT-OPEN, exhibiting performance drops of at least 15 F1. In addition, analysis of the evidence in SCIFACT-OPEN reveals interesting phenomena likely to appear when claim verification systems are deployed in practice, e.g., cases where the evidence supports only a special case of the claim. Our dataset is available at https://github.com/dwadden/scifact-open.

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引用它的顶会 Paper21

  1. FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationSewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis 等EMNLP 2023 · 225 次引用
  2. Long-form factuality in large language modelsJerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu 等NeurIPS 2024 · 182 次引用
  3. HALoGEN: Fantastic LLM Hallucinations and Where to Find ThemAbhilasha Ravichander, Shrusti Ghela, David Wadden, Yejin ChoiACL 2025 · 35 次引用
  4. Towards Understanding Factual Knowledge of Large Language ModelsXuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo 等ICLR 2024 · 21 次引用
  5. Longitudinal Assessment of Reference Quality on WikipediaAitolkyn Baigutanova, Jaehyeon Myung, Diego Sáez-Trumper, Ai-Jou Chou 等WWW 2023 · 14 次引用

它引用的 Paper12

  1. S2ORC: The Semantic Scholar Open Research CorpusKyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney 等ACL 2020 · 424 次引用
  2. AmbigQA: Answering Ambiguous Open-domain QuestionsSewon Min, Julian Michael, Hannaneh Hajishirzi, Luke ZettlemoyerEMNLP 2020 · 162 次引用
  3. Dense Passage Retrieval for Open-Domain Question AnsweringVladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis 等EMNLP 2020 · 142 次引用
  4. MS2: Multi-Document Summarization of Medical StudiesJay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl 等EMNLP 2021 · 83 次引用
  5. Evidence-based Fact-Checking of Health-related ClaimsMourad Sarrouti, Asma Ben Abacha, Yassine Mrabet, Dina Demner-FushmanEMNLP Findings 2021 · 71 次引用

相关 Paper

  1. SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific TablesXinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov 等EMNLP 2023 · 7 次引用
  2. NSF-SciFy: Mining the NSF Awards Database for Scientific ClaimsDelip Rao, Weiqiu You, Eric Wong, Chris Callison-BurchACL 2026 · 1 次引用
  3. Joint Verification and Reranking for Open Fact Checking Over TablesMichael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis 等ACL 2021
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