Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancy
cs.CL, cs.AI
Submitted: 2026-08-02
Updated: 2026-08-31
Comments: 21 pages, 7 figures, 14 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Intelligence Without Integrity: Why Capable LLMs May Undermine Reliability
- gpt-oss-120b & gpt-oss-20b Model Card
- Training language models to follow instructions with human feedback
- Measuring Faithfulness in Chain-of-Thought Reasoning
- Towards Understanding Sycophancy in Language Models
- Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMs
- EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
- Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs
- MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports
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