感知到的LLM政治偏见会降低说服能力
Perceived Political Bias in LLMs Reduces Persuasive Abilities
AI总结：
研究发现，感知到的LLM政治偏见会降低其在对话中的说服效果，表明说服力受政治中立性感知的影响。
摘要：
对话式AI已被提出作为纠正公众误解和传播虚假信息的可扩展方法。然而其有效性可能取决于对其政治中立性的感知。随着LLM进入党派冲突，精英们越来越多地将它们描述为意识形态上偏袒的。我们测试这些可信度攻击是否会减少基于LLM的说服力。在一项预注册的美国调查实验（N=2144）中，参与者完成了一次三次轮次的与ChatGPT关于个人持有的经济政策误解的对话。与中性对照组相比，一条表明LLM偏袒受访者政党立场的简短信息使说服力降低了28%。对话分析表明，警告会改变互动：受访者更倾向于反驳并更不积极地参与。这些发现表明，对话式AI的说服影响是政治上依赖的，受到对党派倾向感知的限制。
英文摘要：
Conversational AI has been proposed as a scalable way to correct public misconceptions and spread misinformation. Yet its effectiveness may depend on perceptions of its political neutrality. As LLMs enter partisan conflict, elites increasingly portray them as ideologically aligned. We test whether these credibility attacks reduce LLM-based persuasion. In a preregistered U.S. survey experiment (N=2144), participants completed a three-round conversation with ChatGPT about a personally held economic policy misconception. Compared to a neutral control, a short message indicating that the LLM was biased against the respondent's party attenuated persuasion by 28%. Transcript analysis indicates that the warnings alter the interaction: respondents push back more and engage less receptively. These findings suggest that the persuasive impact of conversational AI is politically contingent, constrained by perceptions of partisan alignment.
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。
