The emergence of tools based on large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public attention owing to their advanced natural language generation capabilities. These remarkably natural-sounding tools have the potential to be highly useful for various tasks. However, they also tend to produce false, erroneous or misleading content—commonly referred to as hallucinations. Moreover, LLMs can be misused to generate convincing, yet false, content and profiles on a large scale, posing a substantial societal challenge by potentially deceiving users and spreading inaccurate information. This makes fact-checking increasingly important. Despite their issues with factual accuracy, LLMs have shown proficiency in various subtasks that support fact-checking, which is essential to ensure factually accurate responses. In light of these concerns, we explore issues related to factuality in LLMs and their impact on fact-checking. We identify key challenges, imminent threats and possible solutions to these factuality issues. We also thoroughly examine these challenges, existing solutions and potential prospects for fact-checking. By analysing the factuality constraints within LLMs and their impact on fact-checking, we aim to contribute to a path towards maintaining accuracy at a time of confluence of generative artificial intelligence and misinformation.

基于大语言模型的工具的出现（LLMs ），例如 OpenAI 的 ChatGPT 和 Google 的 Gemini，由于其先进的自然语言生成能力而获得了巨大的公众关注。这些听起来非常自然的工具有可能对各种任务非常有用。然而，它们也往往会产生虚假、错误或误导性的内容——通常称为幻觉。而且，LLMs可能会被滥用来大规模生成令人信服但虚假的内容和简介，从而可能欺骗用户和传播不准确的信息，从而带来巨大的社会挑战。这使得事实核查变得越来越重要。尽管他们在事实准确性方面存在问题，LLMs已表现出对支持事实核查的各种子任务的熟练程度，这对于确保事实准确的答复至关重要。鉴于这些担忧，我们探讨了与事实相关的问题LLMs及其对事实核查的影响。我们确定了这些现实问题的关键挑战、迫在眉睫的威胁和可能的解决方案。我们还彻底研究了这些挑战、现有解决方案和事实核查的潜在前景。通过分析其中的事实约束LLMs及其对事实核查的影响，我们的目标是在生成人工智能和错误信息融合的时代，为保持准确性做出贡献。