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University of Florida
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Identifying HIV-related stigma in clinical notes is now possible with LLMs, potentially improving mental health care and treatment outcomes for people living with HIV.
Clinical NLP models can now achieve better transfer learning performance than LoRA with orders of magnitude fewer parameters by distilling knowledge into a single, shared "metaprompt."
Medical LLMs using retrieval augmentation (RAG) can now screen clinical trial candidates with high accuracy, but only for criteria requiring reasoning across long documents.
Forget fine-tuning: merging models in weight space lets you adapt LLMs to new domains without sacrificing instruction-following ability, even with limited data.
