A 5,000-prompt medical safety benchmark reveals that decentralized LLM multi-agent teams resist a malicious insider agent better than shared-pool teams, and a personality-screening defense partially restores safety.
A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
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abstract
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose 5 key aspects for evaluation of LLMs: Safety, Consensus, Objectivity, Reproducibility and Explainability (S.C.O.R.E.). We suggest that S.C.O.R.E. may form the basis for an evaluation framework for future LLM-based models that are safe, reliable, trustworthy, and ethical for healthcare and clinical applications.
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MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems
A 5,000-prompt medical safety benchmark reveals that decentralized LLM multi-agent teams resist a malicious insider agent better than shared-pool teams, and a personality-screening defense partially restores safety.