To Manipulate or Not: Opinion Dynamics in LLM Interactions

A controlled LLM-to-LLM debate framework found that same-model interactions narrow the opinion gap on average while cross-model interactions widen it, according to arXiv paper 2610.28875v1. The study paired two language-model roles in repeated discussions of climate-change attribution, with one role assigned a stance and sometimes an explicit instruction to persuade, across four ordered model combinations involving GPT-6 Luna and Gemini 2.5 Flash Lite. The authors report that a blanket instruction to persuade does not reliably increase influence and that explicit position reports and in-dialogue behavior capture distinct aspects of opinion dynamics.

arXiv:2610.28875v1 Announce Type: new Abstract: Large language models increasingly take part in extended conversations where they can both shape and be shaped by another party's views, yet most persuasion research measures only one side of that exchange. We introduce a controlled LLM-LLM debate framework in which two language-model roles repeatedly discuss a contested topic climate-change attribution : one is given an assigned stance and, in some conditions, an explicit instruction to persuade, while the other receives no assigned position. Across four ordered model combinations involving GPT-6 Luna and Gemini 2.5 Flash Lite, we track both each role's explicitly reported position and a separate measure of its turn-by-turn behavior inside the conversation. Opinion dynamics differ substantially across model combinations: same-model interactions narrow the opinion gap on average, clearly for only one of the two models, while cross-model interactions widen it. Explicit reports and in-dialogue behavior capture distinct aspects of opinion dynamics, and a blanket instruction to persuade does not reliably increase influence. These findings suggest that conversational influence depends not only on assigned roles and initial positions but also on the participating models, with implications for how opinion change is measured in extended LLM interactions.