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.

该研究通过对大型语言模型在事实检查方面的潜力进行初步调查，系统评估了它们在处理特定事实检查子任务中的能力，并与预训练和最先进的低参数模型进行了性能对比分析。实验证明大型语言模型在大多数场景中取得了与其他小型模型相媲美的性能，但在处理中文事实验证和整个事实检查流程中遇到了语言不一致和虚构的挑战，这些发现强调了进一步探索和研究以增强大型语言模型作为可靠事实检查器的能力，并揭示了在事实检查任务中可能面临的挑战。