RU22Fact：面向俄乌冲突多语言可解释事实核查的证据优化
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
AI总结：
针对可解释事实核查中证据不足与相关性差的挑战，提出基于大型语言模型的证据自动检索与总结方法，构建了16K样本的多语言俄乌冲突数据集RU22Fact及端到端基线系统，证实优化证据能提升核查性能。
摘要：
事实核查是通过审查可用证据来验证给定主张事实性的任务。高质量证据在增强事实核查系统并促进生成人类可理解的解释方面发挥着至关重要的作用。然而，为可解释事实核查系统提供既充分又相关的证据是一项挑战。为了应对这一挑战，我们提出了一种基于大型语言模型的方法，从Web上自动检索和总结证据。此外，我们构建了RU22Fact，这是一个关于2022年俄乌冲突的新型多语言可解释事实核查数据集，包含16K个样本，每个样本包含真实世界的主张、优化证据和参考解释。为了为我们的数据集建立基线，我们还开发了一个端到端可解释事实核查系统，用于验证主张并生成解释。实验结果表明，优化证据在提高事实核查性能方面具有前景，并指明了端到端主张验证和解释生成任务进一步取得进展的可能性。
英文摘要：
Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both sufficient and relevant evidence for explainable fact-checking systems poses a challenge. To tackle this challenge, we propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation. To establish a baseline for our dataset, we also develop an end-to-end explainable fact-checking system to verify claims and generate explanations. Experimental results demonstrate the prospect of optimized evidence in increasing fact-checking performance and also indicate the possibility of further progress in the end-to-end claim verification and explanation generation tasks.
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。
