当大型语言模型比激励的人类更具说服力，以及为何如此
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
AI总结：
研究发现，大型语言模型在特定情境下比激励的人类更具说服力，这可能源于其更高的自信表达。
摘要：
大型语言模型（LLMs）已被证明具有高度说服力，但何时以及为何它们优于人类仍然是一个开放性问题。我们比较了两个LLM（Claude 3.5 Sonnet和DeepSeek v3）与有激励说服目标的人类在交互式、实时对话环境中说服力。我们证明LLM的说服优势是情境依赖的：它取决于说服尝试是否诚实（朝向正确答案）或欺骗（朝向错误答案），以及LLM模型，并在重复互动中减弱（不像人类说服力）。在我们的首次大规模实验中，人类与LLM（Claude 3.5 Sonnet）与完成在线测验以获得奖励的人类互动，试图说服他们朝向给定的答案（正确或错误）。Claude在诚实和欺骗情境中都比激励的人类说服者更具说服力，并且如果说服是诚实的，它显著提高了准确性，但如果说服是欺骗性的，则降低了准确性。在后续实验中使用DeepSeek v3，我们复制了关于准确性的发现，但发现只有在说服是欺骗性时，LLM的说服力才更大。对说服者文本的语言分析表明，这些效果可能是由于LLM比人类表达出更高的信心。
英文摘要：
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (Claude 3.5 Sonnet and DeepSeek v3) against humans who had incentives to persuade, using an interactive, real-time conversational setting. We demonstrate that LLMs persuasive superiority is context-dependent: it depends on whether the persuasion attempt is truthful (towards the right answer) or deceptive (towards the wrong answer) and on the LLM model, and wanes over repeated interactions (unlike human persuasiveness). In our first large-scale experiment, humans vs LLMs (Claude 3.5 Sonnet) interacted with other humans who were completing an online quiz for a reward, attempting to persuade them toward a given (either correct or incorrect) answer. Claude was more persuasive than incentivized human persuaders both in truthful and deceptive contexts and it significantly increased accuracy if persuasion was truthful, but decreased it if persuasion was deceptive. In a follow-up experiment with Deepseek v3, we replicated the findings about accuracy but found greater LLM persuasiveness only if the persuasion was deceptive. Linguistic analyses of the persuaders texts suggest that these effects may be due to LLMs expressing higher conviction than humans.
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。
