LLMs drop from 71.1% to 38.0% accuracy on medical questions when misleading context is injected, measured via new MedMisBench benchmark with 10,932 items.
Kovacheva, and Daniel Shu Wei Ting
2 Pith papers cite this work. Polarity classification is still indexing.
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ARSM-Agent cuts attack success rate to 8.7% and reaches 0.91 knowledge consistency in medical tasks by linking risk perception, evidence retrieval, consistency verification, and confidence reweighting.
citing papers explorer
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Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
LLMs drop from 71.1% to 38.0% accuracy on medical questions when misleading context is injected, measured via new MedMisBench benchmark with 10,932 items.
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Research on Security Enhancement Methods for Adversarial Robust Large Language Model Intelligent Agents for Medical Decision-Making Tasks
ARSM-Agent cuts attack success rate to 8.7% and reaches 0.91 knowledge consistency in medical tasks by linking risk perception, evidence retrieval, consistency verification, and confidence reweighting.