在嘈杂人群中导航：为事实验证寻找关键信息
Navigating the Noisy Crowd: Finding Key Information for Claim Verification
AI总结：
针对事实验证中证据与声明嘈杂的问题，提出EACon框架，通过关键词提取与模糊匹配抽象证据，并解构声明为子声明分别验证，显著提升LLM性能。
摘要：
事实验证是一项基于多条证据评估给定声明真实性的任务。使用大型语言模型（LLMs）进行事实验证是一种有前景的方法。然而，简单地将所有证据片段输入LLM并询问声明是否属实，并不能产生良好结果。挑战在于证据和声明两者的嘈杂特性：证据段落通常包含无关信息，关键事实隐藏在上下文中，而声明往往同时传达多个方面。为了在信息的“嘈杂人群”中导航，我们提出了EACon（证据抽象与声明解构），这是一个旨在证据中寻找关键信息并分别验证声明每个方面的框架。EACon首先从声明中提取关键词，并采用模糊匹配为每条原始证据选择相关关键词。这些关键词作为指南，提取并总结关键信息为抽象证据。随后，EACon将原始声明解构为子声明，然后分别对照抽象证据和原始证据进行验证。我们使用两个开源LLM在两个具有挑战性的数据集上评估EACon。结果表明，EACon持续且显著地提高了LLM在事实验证中的性能。
英文摘要：
Claim verification is a task that involves assessing the truthfulness of a given claim based on multiple evidence pieces. Using large language models (LLMs) for claim verification is a promising way. However, simply feeding all the evidence pieces to an LLM and asking if the claim is factual does not yield good results. The challenge lies in the noisy nature of both the evidence and the claim: evidence passages typically contain irrelevant information, with the key facts hidden within the context, while claims often convey multiple aspects simultaneously. To navigate this "noisy crowd" of information, we propose EACon (Evidence Abstraction and Claim Deconstruction), a framework designed to find key information within evidence and verify each aspect of a claim separately. EACon first finds keywords from the claim and employs fuzzy matching to select relevant keywords for each raw evidence piece. These keywords serve as a guide to extract and summarize critical information into abstracted evidence. Subsequently, EACon deconstructs the original claim into subclaims, which are then verified against both abstracted and raw evidence individually. We evaluate EACon using two open-source LLMs on two challenging datasets. Results demonstrate that EACon consistently and substantially improve LLMs' performance in claim verification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
