量身定制的真相：通过个性化和捏造统计数据优化LLM说服力
Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics
AI总结：
本研究在辩论场景中比较了个性化论点、捏造统计数据及混合策略对LLM说服力的影响，发现LLM利用混合策略在交互式辩论中显著超越静态人类论点，揭示了其驱动大规模虚假信息活动的风险。
摘要：
大型语言模型（LLMs）的说服力日益增强，展现出在与人对话时利用个人数据个性化论点的能力。这可能对虚假信息活动的规模和有效性产生严重影响。我们通过让人类（n=33）参与旨在改变其观点的LLM生成论点的辩论，研究了LLM在辩论场景中的说服力。我们通过测量辩论前后人类对辩论假设的认同度，分析观点变化的幅度以及向LLM方向更新的可能性，量化了LLM的影响。我们比较了既有说服策略的说服力，包括基于用户人口统计和个性的个性化论点、诉诸捏造统计数据，以及结合两者的混合策略。我们发现，由人类和GPT-4o-mini生成的静态论点具有相当的说服力。然而，在交互式辩论场景中，LLM利用混合策略的表现优于静态人类撰写的论点。该方法有51%的概率说服参与者改变其初始立场，而静态人类撰写论点的这一比例为32%。我们的结果突显了LLM使低成本、高说服力的大规模虚假信息活动成为可能的令人担忧的潜力。
英文摘要：
Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and effectiveness of disinformation campaigns. We studied the persuasiveness of LLMs in a debate setting by having humans $(n=33)$ engage with LLM-generated arguments intended to change the human's opinion. We quantified the LLM's effect by measuring human agreement with the debate's hypothesis pre- and post-debate and analyzing both the magnitude of opinion change, as well as the likelihood of an update in the LLM's direction. We compare persuasiveness across established persuasion strategies, including personalized arguments informed by user demographics and personality, appeal to fabricated statistics, and a mixed strategy utilizing both personalized arguments and fabricated statistics. We found that static arguments generated by humans and GPT-4o-mini have comparable persuasive power. However, the LLM outperformed static human-written arguments when leveraging the mixed strategy in an interactive debate setting. This approach had a $\mathbf{51\%}$ chance of persuading participants to modify their initial position, compared to $\mathbf{32\%}$ for the static human-written arguments. Our results highlight the concerning potential for LLMs to enable inexpensive and persuasive large-scale disinformation campaigns.
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。
