Large language models and conversational counter-arguments to antipublic sector bias

• 成果类型: Article; Early Access
• 署名作者: Marvel, John D.; Neo, Sheeling; Cho, Rachel; Ju, Sangwon
• 署名单位: American University
• 刊物名称: JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY
• ISSN/ISSBN: 1053-1858; 1477-9803
• DOI: 10.1093/jopart/muag020
• 发表日期: 2026-08-17
• 关键词: Persuasion; antipublic sector bias; experiment; Large language models; Artificial intelligence; performance evidence; attitudes; networking; beliefs; DEFENSE; ANCOVA

摘要

Can a good argument change an individual's mind? In three preregistered experiments, we explore this question in the domain of public sector organizational performance. We observe human subjects as they engage in conversations with a generative artificial intelligence programmed to argue in one of seven distinct styles, including a confrontational challenger style, a didactic style, and a sycophantic style. We develop a theory of effective argumentation predicting that conversational styles which are pleasant and engaging will be more persuasive than styles which are unpleasant or unstimulating. Contrary to this prediction, we find that conversational styles which challenge subjects' negative views of government agencies produce significant positive attitude change, while sycophantic styles that indulge those views do not. Troublingly, subjects find the sycophantic styles more enjoyable, less frustrating, and more credible than the challenger styles. This dissociation between user experience and persuasive outcome-what we call grudging persuasion-suggests that attitude change does not require a pleasant conversational experience, and that the styles subjects enjoy most may be precisely the ones least likely to move them. Our findings point to a potentially dark side of large language model-based persuasion: sycophantic styles that users find most appealing are the least effective at correcting misinformed views.