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.

翻译：针对现实世界主张的通用事实核查系统在收集有效且充分的实时证据以及做出合理决策方面面临重大挑战。本研究提出开放域可解释事实核查（OE-Fact）系统，用于现实场景中的主张验证。该系统能够利用大语言模型强大的理解与推理能力验证主张，并为事实核查决策生成因果解释。为将传统三模块事实核查框架适配至开放域场景，我们首先从开放网站检索与主张相关的信息作为证据，随后通过大语言模型与相似度计算保留与主张相关的证据用于后续验证。我们在事实提取与验证（FEVER）数据集上评估了适配后的三模块OE-Fact系统性能。实验结果表明，我们的OE-Fact系统在封闭域与开放域场景中均优于通用事实核查基线系统，能够在保证稳定准确判决的同时，为事实核查决策提供简洁且具有说服力的实时解释。