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most citedAddressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

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cs.LG2026★ 2 cited

Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming

Jiazhen Pan, Bailiang Jian, Paul Hager +19

The paper presents a dynamic red‑teaming framework (DAS) that continuously stress‑tests large language models on health tasks for robustness, privacy, bias, and hallucination, reve…