LLM能否通过提出相关问题改进多模态事实核查？
Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?
AI总结：
针对大语言模型事实核查缺少问题拟定环节的局限，提出LRQ-FACT框架用LLM生成事实核查问题以辅助多模态证据检索与核查，实验验证其有效性并分析各组件作用。
摘要：
传统事实核查依赖人工拟定相关且有针对性的事实核查问题（FCQ）、检索证据并验证声明的真实性。尽管大语言模型（LLM）已被广泛用于大规模自动化证据检索和真实性验证，但由于缺少FCQ拟定环节，其在事实核查中的效果受到限制。为填补这一空白，我们试图回答两个研究问题：（1）LLM能否生成相关的FCQ？（2）LLM生成的FCQ能否改进多模态事实核查？为此我们提出LRQ-FACT框架，该框架利用LLM生成相关FCQ，通过探查多模态信息来辅助证据检索、增强事实核查效果。通过大量实验，我们验证了LRQ-FACT能否生成不同类型的相关FCQ，以及其在多模态事实核查中能否持续优于基线方法。进一步分析阐明了LRQ-FACT各组件提升事实核查性能的作用机制。
英文摘要：
Traditional fact-checking relies on humans to formulate relevant and targeted fact-checking questions (FCQs), search for evidence, and verify the factuality of claims. While Large Language Models (LLMs) have been commonly used to automate evidence retrieval and factuality verification at scale, their effectiveness for fact-checking is hindered by the absence of FCQ formulation. To bridge this gap, we seek to answer two research questions: (1) Can LLMs generate relevant FCQs? (2) Can LLM-generated FCQs improve multimodal fact-checking? We therefore introduce a framework LRQ-FACT for using LLMs to generate relevant FCQs to facilitate evidence retrieval and enhance fact-checking by probing information across multiple modalities. Through extensive experiments, we verify if LRQ-FACT can generate relevant FCQs of different types and if LRQ-FACT can consistently outperform baseline methods in multimodal fact-checking. Further analysis illustrates how each component in LRQ-FACT works toward improving the fact-checking performance.
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。
