使用大型语言模型进行基于证据的可解释开放域事实核查
Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
AI总结：
本文提出OE-Fact系统，结合开放网站证据检索、LLM筛选与推理验证，在FEVER上实现封闭域和开放域中更准确、可解释的实时事实核查。
摘要：
面向现实世界声明的通用事实核查系统在收集有效且充分的实时证据以及作出有理有据的决策方面面临重大挑战。本文提出用于现实场景中声明核查的开放域可解释事实核查（OE-Fact）系统。OE-Fact系统可利用大型语言模型（LLM）强大的理解与推理能力验证声明，并为事实核查决策生成因果解释。为使传统三模块事实核查框架适应开放域设定，我们首先从开放网站检索与声明相关的信息作为相关证据；随后通过LLM和相似度计算保留与声明相关的证据，用于后续验证。我们在事实提取与验证（FEVER）数据集上评估了改造后的三模块OE-Fact系统性能。实验结果表明，OE-Fact系统在封闭域和开放域场景中均优于通用事实核查基线系统，能够确保稳定准确的裁决，同时为事实核查决策提供简洁且有说服力的实时解释。
英文摘要：
Universal fact-checking systems for real-world claims face significant challenges in gathering valid and sufficient real-time evidence and making reasoned decisions. In this work, we introduce the Open-domain Explainable Fact-checking (OE-Fact) system for claim-checking in real-world scenarios. The OE-Fact system can leverage the powerful understanding and reasoning capabilities of large language models (LLMs) to validate claims and generate causal explanations for fact-checking decisions. To adapt the traditional three-module fact-checking framework to the open domain setting, we first retrieve claim-related information as relevant evidence from open websites. After that, we retain the evidence relevant to the claim through LLM and similarity calculation for subsequent verification. We evaluate the performance of our adapted three-module OE-Fact system on the Fact Extraction and Verification (FEVER) dataset. Experimental results show that our OE-Fact system outperforms general fact-checking baseline systems in both closed- and open-domain scenarios, ensuring stable and accurate verdicts while providing concise and convincing real-time explanations for fact-checking decisions.
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。
