Abstract
Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and corporate privacy. In this work, we propose MedAdapter, a unified post-hoc adapter for test-time adaptation of LLMs towards biomedical applications. Instead of fine-tuning the entire LLM, MedAdapter effectively adapts the original model by fine-tuning only a small BERT-sized adapter to rank candidate solutions generated by LLMs. Experiments demonstrate that MedAdapter effectively adapts both white-box and black-box LLMs in biomedical reasoning, achieving average performance improvements of 25.48% and 11.31%, respectively, without requiring extensive computational resources or sharing data with third parties. MedAdapter also yields superior performance when combined with train-time adaptation, highlighting a flexible and complementary solution to existing adaptation methods. Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.
Abstract (translated)
尽管生成和推理能力有所提高，将大型语言模型（LLMs）适应生物医学领域仍然具有挑战性，因为它们具有巨大的规模和企业隐私。在本文中，我们提出了MedAdapter，一种统一的后置适配器，用于在测试时对LLMs进行生物医学应用的适应。我们不是对整个LLM进行微调，而是通过微调只有BERT大小的适配器来有效地适应原始模型。实验证明，MedAdapter有效地将白盒和黑盒LLM在生物医学推理中进行适应，分别实现了平均性能提高25.48%和11.31%。与不需要大量计算资源或与第三方共享数据相比，MedAdapter还具有卓越的性能。当结合训练时适应时，MedAdapter更加凸显了其对现有适应方法的一个灵活且互补的解决方案。面对模型性能、计算资源和数据隐私的挑战，MedAdapter为将LLMs适应生物医学领域提供了高效、隐私保护、成本低廉和透明的解决方案。
URL
https://arxiv.org/abs/2405.03000
