MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning
Accepted at EMNLP'24,
Abstract
MedAdapter presents an efficient test-time adaptation method for applying large language models to medical reasoning tasks. Rather than finetuning the base LLM, MedAdapter trains a lightweight adapter module that operates at inference time, improving downstream accuracy on medical QA, diagnosis, and reasoning benchmarks while remaining compatible with black-box commercial LLMs.
Materials
BibTeX
@inproceedings{xu2024medadapter,
title={MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning},
author={Ran Xu and Yuchen Zhuang and Yue Yu and Haotian Sun and Hang Wu and Carl Yang and May D. Wang},
booktitle={Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2024}
}