大型语言模型是否擅长事实核查：一项初步研究
Are Large Language Models Good Fact Checkers: A Preliminary Study
AI总结：
本文初步评估了大型语言模型在事实核查子任务中的能力，发现其性能可与小型先进模型竞争，但在中文核查和完整流程中仍受幻觉及语言不一致问题制约。
摘要：
近年来，大型语言模型（LLMs）因其出色的推理能力和庞大的知识库而受到广泛关注，与其他语言模型相比，它们在处理各种自然语言处理任务方面表现更为优越。本文对LLMs在事实核查中的潜力进行了初步研究。本研究旨在全面评估各种LLMs在处理特定事实核查子任务中的表现，系统性地评估其能力，并将其性能与预训练模型和最先进的低参数模型进行对比分析。实验表明，在大多数场景下，LLMs相比其他小型模型取得了具有竞争力的性能。然而，由于语言不一致性和幻觉问题，它们在有效处理中文事实核查以及完整事实核查流程方面遇到了挑战。这些发现强调了进一步探索和研究的必要性，以提升LLMs作为可靠事实核查工具的熟练度，同时揭示了LLMs的潜在能力以及在事实核查任务中可能面临的挑战。
英文摘要：
Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as superior in handling various natural language processing tasks compared to other language models. In this paper, we present a preliminary investigation into the potential of LLMs in fact-checking. This study aims to comprehensively evaluate various LLMs in tackling specific fact-checking subtasks, systematically evaluating their capabilities, and conducting a comparative analysis of their performance against pre-trained and state-of-the-art low-parameter models. Experiments demonstrate that LLMs achieve competitive performance compared to other small models in most scenarios. However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations. These findings underscore the need for further exploration and research to enhance the proficiency of LLMs as reliable fact-checkers, unveiling the potential capability of LLMs and the possible challenges in fact-checking 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。
