wshi83/MedAdapter
[EMNLP'24] MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning
This project helps medical professionals and researchers improve the accuracy of large language models (LLMs) for medical reasoning tasks. By using this, you can take an existing LLM and medical data (like patient notes, research papers, or clinical guidelines) to generate more reliable medical answers or insights. It's designed for medical domain experts who use AI tools and need to ensure their outputs are clinically sound.
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Use this if you need to adapt general-purpose large language models to provide highly accurate and medically sound responses for healthcare or research applications without extensive fine-tuning.
Not ideal if your primary goal is general natural language processing unrelated to the medical domain, or if you need to build an LLM from scratch rather than adapting an existing one.
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Python
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Last pushed
Dec 26, 2024
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