大模型查假新闻能力弱，还偏爱左倾来源。
How LLMs Fail to Support Fact-Checking
- 用两步推理提示让模型先找可信源再写回应
- 模型难引用真实新闻，且倾向左倾信源
- 不同模型回应多样性差异大，需更强防护
尽管大型语言模型（LLMs）可能加剧网络虚假信息传播，但也展现出应对虚假信息的潜力。本文实证研究了ChatGPT、Gemini和Claude三款模型在反驳政治虚假信息方面的能力。我们采用两步链式思维提示方法：模型首先为给定声明识别可信信息来源，随后生成有说服力的回应。研究发现，这些模型难以将其回应建立在真实新闻源之上，且更倾向于引用偏向左翼的来源。同时，各模型间回应多样性存在显著差异。结果表明，仅通过提示工程无法可靠实现事实核查，亟需更稳健的约束机制。研究对研究人员及非技术用户均有重要启示。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) can amplify online misinformation, they also show promise in tackling misinformation. In this paper, we empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering political misinformation. We implement a two-step, chain-of-thought prompting approach, where models first identify credible sources for a given claim and then generate persuasive responses. Our findings suggest that models struggle to ground their responses in real news sources, and tend to prefer citing left-leaning sources. We also observe varying degrees of response diversity among models. Our findings highlight concerns about using LLMs for fact-checking through only prompt-engineering, emphasizing the need for more robust guardrails. Our results have implications for both researchers and non-technical users.
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