OpenMedLM:快速工程可以通过开源的大型语言模型超越医学问答中的微调性能
Jenish Maharjan1, Anurag Garikipati1, Navan Preet Singh1
1Montera, Inc. Dba Forta, 548 Market St., PMB 89605, San Francisco, CA, 94104-5401, USA.
Scientific reports
|June 19, 2024
概括
开源大型语言模型 (LLM) 使用快速工程实现了最先进的医疗性能. OpenMedLM证明了可访问的模型可以在医疗保健中脱而出,而无需昂贵的微调.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 通过大型语言模型 (LLM) 获得专业医疗知识的公平准入受微调要求和专有准入限制.
- 开源 (OS) 医学LLM提供了对医疗保健应用程序至关重要的提高透明度和合规性.
研究的目的:
- 介绍OpenMedLM,一个旨在提高OS LLM在医疗基准上的表现的提示平台.
- 为了证明OS基础模型可以在医疗任务中实现最先进的 (SOTA) 性能,而不需要专门的微调.
主要方法:
- 在医学基准 (MedQA,MedMCQA,PubMedQA,MMLU医学子集) 上评估OS基础LLMs (7B-70B).
- 使用Yi34B模型和各种提示策略 (零射击,少数射击,思想链,整体/自我一致性投票) 开发OpenMedLM.
主要成果:
- 在三个医学LLM基准标准上,OpenMedLM取得了OS SOTA的结果,超过了以前精心调整的OS模型.
- 在MedQA上获得了72.6%的准确度 (比之前的SOTA提高了2.4%),在MMLU医疗子组获得了81.7%,在OS LLM中首次超过80%.
- 在OS LLMs中证明了医学特异性的新兴性质.
结论:
- 快速工程可以显著优化医疗应用可访问的OS LLM的性能.
- 当通过快速工程来增强OS LLM时,可以有效地完成医疗保健任务,验证其实用性和可访问性.
- 在使强大的医疗人工智能工具更广泛地可用方面,OpenMedLM代表了重大进步.
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