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To Manipulate or Not: Opinion Dynamics in LLM Interactions

Summary

A new paper introduces a controlled LLM-to-LLM debate framework to study how models shape and are shaped by each other's views over extended conversations. It finds that same-model interactions narrow opinion gaps while cross-model interactions widen them, and that a blanket instruction to persuade does not reliably increase influence.

SourcearXiv AIAuthor: Mohammad Siahkamari, Phuoc Dat Nguyen, Stephen MacNeil
To Manipulate or Not: Opinion Dynamics in LLM Interactions
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[Submitted on 20 Oct 2026]

Title:To Manipulate or Not: Opinion Dynamics in LLM Interactions

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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.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)

Cite as: arXiv:2610.28875 [cs.AI]

(or arXiv:2610.28875v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.28875

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mohammad Siahkamari [view email] [v1] Tue, 20 Oct 2026 19:48:20 UTC (827 KB)

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Key points

  • The study uses a two-role LLM debate setup on climate-change attribution, with one model assigned a stance and sometimes told to persuade, while the other receives no position.
  • Across four ordered combinations of GPT-6 Luna and Gemini 2.5 Flash Lite, same-model interactions narrowed opinion gaps on average (clearly for only one model), while cross-model interactions widened them.
  • Explicitly reported positions and turn-by-turn in-dialogue behavior capture distinct aspects of opinion dynamics.
  • A blanket persuasion instruction did not reliably boost influence, suggesting conversational influence depends on the specific models involved.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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