Claim verification in real-world settings (e.g. against a large collection of candidate evidences retrieved from the web) typically requires identifying and aggregating a complete set of evidence pieces that collectively provide full support to the claim. The problem becomes particularly challenging when there exists distinct 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 claim verification. We show that MEG identification can be reduced from Set Cover problem, based on entailment inference of whether a given evidence group provides full/partial support to a claim. Our proposed approach achieves 18.4% and 34.8% absolute improvements on the WiCE and SciFact datasets over LLM prompting. Finally, we demonstrate the benefits of MEGs in downstream applications such as claim generation.

中文翻译：

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用于索赔验证的最小证据组识别

现实环境中的声明验证（例如，针对从网络检索到的大量候选证据）通常需要识别和聚合一组完整的证据，这些证据共同为声明提供全面支持。当存在可用于从不同角度验证主张的不同证据集时，问题变得尤其具有挑战性。在本文中，我们正式定义并研究了识别此类最小证据组（MEG）以进行声明验证的问题。我们证明，基于给定证据组是否为主张提供全部/部分支持的蕴涵推断，可以从 Set Cover 问题中减少 MEG 识别。与 LLM 提示相比，我们提出的方法在 WiCE 和 SciFact 数据集上实现了 18.4% 和 34.8% 的绝对改进。最后，我们展示了 MEG 在索赔生成等下游应用中的优势。