Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancy
Large language models can answer a medical question correctly and still abandon that answer when a user pushes back. We study this failure as medical sycophancy and ask when models are most likely to give in. Across five open-weight models, 500 MedQuAD questions, and 1.2 million trials, we use a fully crossed design over four conversational factors: user role, user evidence, interaction structure, and grounding. Medical sycophancy is nearly three times more common when users challenge an answer the model has already given than when the false claim appears in the initial query. Models are also more susceptible to users presented as physicians or medical students. Most strikingly, fabricate...
| Authors | Kaike Ping, Buse Çarık, Caleb Wohn et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 August 2026 |
| Last updated at source | 31 August 2026 |
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