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Evaluating the Performance and Fragility of Large Language Models on the Self-Assessment for Neurological Surgeons
0
Zitationen
8
Autoren
2025
Jahr
Abstract
While current LLMs demonstrate an impressive ability to answer neurosurgery board-like examination questions, their performance is markedly vulnerable to extraneous, distracting information. These findings underscore the critical need for developing novel mitigation strategies aimed at bolstering LLM resilience against in-text distractions, particularly for safe and effective clinical deployment.
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