Minimal Evidence Group Identification for Claim Verification (2025)

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.

## introduction the task of claim verification predicts whether a claim is supported by the presented evidence @xcite . a claim verification model is expected to identify the correct evidence pieces (eps; e.g. evidence sentences or snippets) among tens of retrieved candidate evidence, but a practical challenge lies in that there might exist multiple sets of evidence that verify the claim from different perspectives. figure 1 shows an example where, given a claim and some retrieved evidence, there exist two different -but both valid -ways of supporting the claim. while humans can quickly identify mutually redundant eps, e.g. e 1 and e 3 in figure 1 , and propose plausible combinations of eps as evidence groups (egs, formally defined in section 3.1), * work performed while the authors are interning at google as phd students on october 17, 2018, one year after downie's death, a previously unreleased studio recording of the song "wait so long" was played on k-rock. ## related work classic claim verification @xcite ) consists of three steps: evidence retrieval, evidence selection, and stance prediction. evidence retrieval perform coarse-grained filtering of eps from thousands of candidates. evidence selection and stance prediction perform fine-grained selection of eps and predict whether the claim is supported by the selected eps. meg identification builds on classic claim verification by restricting evidence selection and stance prediction to predict a minimal group of eps that fully supports the claim. to address the problem that claim verification systems @xcite @xcite the closest work to ours is scifact @xcite , which annotates "minimal evidence sets" for each claim. however, the scifact shared task does not penalize non-minimal egs, and consequently models that evaluate on scifact @xcite @xcite are trained on the union of egs from different human annotators, which is no longer minimal. similarly, @xcite ; @xcite ; @xcite collect (possibly redundant) egs from multiple annotators and use their union as training labels. as a result, existing models prioritize ep recall and are not directly comparable to meg identification models. ## meg identification approach the challenge of meg identification is to find the smallest set of eps that fully supports the claim. as discussed in section 2, classic claim verification models treat the ep as the basic unit; they are algorithm 1 meg identification with a support prediction model. simplified for illustration, see appendix section a.2 for details. require: c, @xmath0, e2, ..., en}, m odel require: max_size ▷ max size of egs to consider. m eg ← [] ▷ proposed megs. for size in 1... min(|e|, max_size) do cs ← makecombinations(c, e, size) ▷ list of notredundant combinations of partially supporting eps. for s in cs do label ← m odel(c, s) if label is f ully support then m eg.append(s) end if end for if len(m eg) > 0 then break end if end for output m eg neither designed nor trained for groups of evidence. our experiments of prompting directly with llms also show poor performance ( as algorithms 1 shows, we decompose meg identification into two steps: (1) predicting whether a candidate set of eps fully supports, partially supports, or does not support the claim and (2) bottomup merging partially supporting groups in search of a fully supporting group. the support prediction model can be implemented by any reasonable approach, such as prompting llms or fine-tuning models like t5 @xcite . when merging groups, we increment the overall group size by 1 at each step. note that if we only evaluate the base case with @xmath1, this is equivalent to classic claim verification @xcite @xcite . based on the definition of meg (section 3.1), we derive three principles to prune the problem space for a tractable solution: (1) any superset of an meg fully supports the claim c; (2) any nonempty subset of an meg partially supports c; and ## proof of np-hardness in this section, we provide a simple proof to show that the meg identification problem is np-hard. ## base model performance a base model solves the base problem of support prediction: predicting whether a candidate set of eps fully supports, partially supports, or does not support the claim. this can be implemented by any reasonable approach but we use an llm-based approach for simplicity. implementation. for both the support prediction model and notredundant checker, we prompt palm-2l @xcite with few-shot demonstrations and greedy decoding. we follow @xcite to select the llm's most confident examples for few-shot demonstrations. to prioritize ## extrinsic evaluation the non-redundancy of megs not only makes the evidence more human-readable, it also improves the performance of downstream applications. inspired by @xcite , we use wice-meg to highlight the meg's minimality and sufficiency properties using claim generation as an example downstream task, with a computation budget measured in the number of words or sentences. ## conclusion we have addressed the challenging scenario in claim verification where a model is expected to identify a minimal group of evidence pieces (eps) among a relatively large amount of candidate evidence, and there might exist different sets of evidence that verify the claim from different perspectives. we formally define and study the problem of such minimal evidence group (meg) identification and show that it can be reduced from a set coverlike problem. our proposed approach achieves significant improvements over direct llm prompting. finally, we demonstrate the benefit of megs over classic claim verification approaches in downstream applications such as claim generation.