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WiCE: Real-World Entailment for Claims in Wikipedia

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arxiv 2303.01432 v2 pith:GGTISPOZ submitted 2023-03-02 cs.CL

Ryo Kamoi , Tanya Goyal , Juan Diego Rodriguez , Greg Durrett This is my paper · ORCID
classification cs.CL
keywords entailmentmodelsclaimwiceclaimsdatasetdatasetsevidence
verification ladder T0 review T1 audit T2 compute T3 formal
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Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represent a significant domain shift from existing entailment datasets, and models underperform as a result. We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim, and a minimal subset of evidence sentences that support each subclaim. To support this, we propose an automatic claim decomposition strategy using GPT-3.5 which we show is also effective at improving entailment models' performance on multiple datasets at test time. Finally, we show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

    cs.CL 2026-07 conditional novelty 7.0 of 10

    Clinical RAG can attribute real evidence about drug Y to queried drug X at high rates under adversarial retrieval, a failure invisible to faithfulness and citation metrics but detectable by entity-attribution verification.

  2. Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

    cs.CL 2025-10 conditional novelty 6.0 of 10

    SLIM separates search and browse tools and summarizes trajectories every 50 turns, beating several open-source agentic search systems on BrowseComp and HLE with fewer tool calls and lower cost.

  3. SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SUCEA improves adversarial fact-checking by decomposing claims into atomic sub-claims, editing each sub-claim toward retrieved evidence, and re-retrieving before predicting the final label.

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