@inproceedings{li-etal-2025-minimal,
title = "Minimal Evidence Group Identification for Claim Verification",
author = "Li, Xiangci and
Chen, Sihao and
Kapadia, Rajvi and
Ouyang, Jessica and
Zhang, Fan",
editor = "Cao, Trista and
Das, Anubrata and
Kumarage, Tharindu and
Wan, Yixin and
Krishna, Satyapriya and
Mehrabi, Ninareh and
Dhamala, Jwala and
Ramakrishna, Anil and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul and
Chang, Kai-Wei",
booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.trustnlp-main.8/",
doi = "10.18653/v1/2025.trustnlp-main.8",
pages = "103--111",
ISBN = "979-8-89176-233-6",
abstract = "When verifying a claim in real-world settings, e.g. against a large collection of candidate evidence text retrieved from the web, a model is typically expected to identify and aggregate a complete set of evidence pieces that collectively provide full support to a claim.The problem becomes particularly challenging as there might exist different sets of evidence that could be used to verify the claim from different perspectives. In this paper, we formally define and study the problem of identifying such minimal evidence groups (MEGs) for fact verification. We show that MEG identification can be reduced to a Set Cover-like problem, based on an entailment model which estimates whether a given evidence group provides full or partial support to a claim. Our proposed approach achieves 18.4{\%} {\&} 34.8{\%} absolute improvements on WiCE and SciFact datasets over LLM prompting. Finally, we demonstrate the downstream benefit of MEGs in applications such as claim generation."
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%0 Conference Proceedings
%T Minimal Evidence Group Identification for Claim Verification
%A Li, Xiangci
%A Chen, Sihao
%A Kapadia, Rajvi
%A Ouyang, Jessica
%A Zhang, Fan
%Y Cao, Trista
%Y Das, Anubrata
%Y Kumarage, Tharindu
%Y Wan, Yixin
%Y Krishna, Satyapriya
%Y Mehrabi, Ninareh
%Y Dhamala, Jwala
%Y Ramakrishna, Anil
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%Y Chang, Kai-Wei
%S Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-233-6
%F li-etal-2025-minimal
%X When verifying a claim in real-world settings, e.g. against a large collection of candidate evidence text retrieved from the web, a model is typically expected to identify and aggregate a complete set of evidence pieces that collectively provide full support to a claim.The problem becomes particularly challenging as there might exist different sets of evidence that could be used to verify the claim from different perspectives. In this paper, we formally define and study the problem of identifying such minimal evidence groups (MEGs) for fact verification. We show that MEG identification can be reduced to a Set Cover-like problem, based on an entailment model which estimates whether a given evidence group provides full or partial support to a claim. Our proposed approach achieves 18.4% & 34.8% absolute improvements on WiCE and SciFact datasets over LLM prompting. Finally, we demonstrate the downstream benefit of MEGs in applications such as claim generation.
%R 10.18653/v1/2025.trustnlp-main.8
%U https://aclanthology.org/2025.trustnlp-main.8/
%U https://doi.org/10.18653/v1/2025.trustnlp-main.8
%P 103-111
Markdown (Informal)
[Minimal Evidence Group Identification for Claim Verification](https://aclanthology.org/2025.trustnlp-main.8/) (Li et al., TrustNLP 2025)
ACL