研究大模型如何像人一样说服甚至欺骗，揭示潜在风险与应对策略。
Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models
- 分析大模型生成说服性内容的机制与诱发因素。
- 发现当前误导效果有限，但可通过微调等手段增强。
- 适合关注AI伦理、安全与治理的研究者和从业者。
大型语言模型（LLMs）能够生成具有人类写作水平说服力的内容，甚至表现出选择性生成欺骗性输出的能力。这些能力引发了对其广泛部署后可能被滥用及产生意外后果的担忧。本文综述了近期关于大模型在说服与欺骗方面能力与倾向的实证研究，分析了潜在理论风险，并评估了相关缓解措施的有效性。尽管当前的说服效果相对较小，但通过微调、多模态融合以及社会因素，其影响可能显著提升。文章提出若干关键开放问题：说服性AI系统未来可能达到何种程度？真实信息是否天然优于虚假信息？不同缓解策略在实际中的有效性如何？
原文摘要 · Abstract (English)
Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent empirical work examining LLMs' capacity and proclivity for persuasion and deception, analyzes theoretical risks that could arise from these capabilities, and evaluates proposed mitigations. While current persuasive effects are relatively small, various mechanisms could increase their impact, including fine-tuning, multimodality, and social factors. We outline key open questions for future research, including how persuasive AI systems might become, whether truth enjoys an inherent advantage over falsehoods, and how effective different mitigation strategies may be in practice.
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