医学适配器:大语言模型的高效测试时间适应医疗推理
Wenqi Shi1, Ran Xu2, Yuchen Zhuang1
1Georgia Tech.
概括
将大型语言模型 (LLM) 适应生物医学用途是很困难的. MedAdapter通过微调小型适配器提供了一个保护隐私的解决方案,有效地提高了生物医学任务的LLM性能.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 为生物医学等专业领域适应大型语言模型 (LLM) 提出了重大挑战.
- 这些挑战包括模型大小,计算要求和关键数据隐私问题.
研究的目的:
- 介绍MedAdapter,一个新的后期适配器,用于有效的测试时间适应生物医学应用中的LLMs.
- 通过只调整一个小的适配器模块来解决完整模型微调的局限性.
主要方法:
- MedAdapter采用统一的后期适配器方法,微调一个BERT大小的适配器,以对LLMs生成的候选解决方案进行排名.
- 该方法在八个不同的数据集中对四个不同的生物医学任务进行了评估,测试了白盒和黑盒LLM场景.
主要成果:
- 在生物医学推理任务中,MedAdapter实现了显著的性能改善,白盒LLM的平均水平为18.24%,黑盒LLM的平均水平为10.96%.
- 该适应方法在不需要大量计算资源或不损害数据隐私的情况下证明了有效性.
- 当MedAdapter与现有的火车时间适应技术相结合时,性能收益进一步提升.
结论:
- MedAdapter提供了一种高效,保护隐私,具有成本效益和透明的解决方案,用于将LLM适应复杂的生物医学领域.
- 这种方法为现有的LLM适应方法提供了一个灵活和补充的策略,平衡性能与资源和隐私限制.
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