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.

翻译：对话式人工智能已被提出作为纠正公众误解和传播错误信息的可扩展方法。然而其有效性可能取决于对其政治中立性的感知。随着大型语言模型进入党派冲突领域，精英阶层日益将其描绘为具有意识形态倾向。我们检验这些可信度攻击是否会削弱基于大型语言模型的劝说效果。在一项预先注册的美国调查实验（N=2144）中，参与者与ChatGPT就个人持有的经济政策误解进行了三轮对话。与中性对照组相比，一条表明大型语言模型对受访者所属党派存在偏见的简短提示使劝说效果降低了28%。对话记录分析表明，警告信息改变了互动模式：受访者更频繁提出质疑且接受度降低。这些发现表明，对话式人工智能的劝说效果具有政治条件性，受限于对其党派倾向的感知。