AI

To Manipulate or Not: Opinion Dynamics in LLM Interactions

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:03:24 UTC.

Briefings

A controlled LLM debate framework was tested using GPT-6 Luna and Gemini 2.5 Flash Lite to measure opinion dynamics during climate-change attribution discussions. The study found that opinion gaps widen when models interact across different systems rather than within the same system, and that explicit persuasion instructions do not reliably increase influence. Results indicate that conversational influence depends on model compatibility and initial positions, challenging previous research that focused solely on one model's perspective.

Key points

• Opinion gaps widen significantly when GPT-6 Luna and Gemini 2.5 Flash Lite interact across different systems compared to same-model interactions.
• Explicit instructions to persuade did not reliably increase influence in the debate framework.

Context

This research addresses a gap in persuasion research by introducing a controlled LLM-LLM debate framework to measure opinion dynamics in extended conversations. The study tracks explicitly reported positions and turn-by-turn behavior across four ordered model combinations involving GPT-6 Luna and Gemini 2.5 Flash Lite to understand how model compatibility affects opinion change.

Limitations

Evidence is limited to a single preprint (arXiv:2610.28875v1) with no peer review or additional validation. Findings are preliminary and may not generalize to real-world extended conversations. Results are based on simulated debates rather than empirical data from actual human-LLM interactions.

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