MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
Sycoeval- em: Sycophancy evaluation of large language models in simulated clinical encounters for emergency care.arXiv preprint arXiv:2601.16529
2 Pith papers cite this work. Polarity classification is still indexing.
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SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
citing papers explorer
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MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors
MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
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SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.