Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers. A key risk is conformity: a model may abandon its own answer simply because others agree on a different one. Prior studies show that LLMs often revise toward a majority answer, but it remains unclear whether these revisions help correct mistakes as often as they introduce new errors. In this paper, we conduct a controlled study in which an LLM first answers a question, then sees simulated peer responses before making a final decision. We manipulate two social cues: consensus structure and authority labels assigned to peers, and measure how they influence beneficial and ...
| Authors | Jiaming Qu, Lucheng Fu, Yibo Hu |
|---|---|
| Primary category | cs.CL |
| Published | 1 June 2026 |
| Last updated at source | 6 June 2026 |
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