MedAlpaca——一个医疗对话式AI模型与训练数据的开源集合
MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data
AI总结：
为满足医疗领域可本地部署的开源大语言模型需求，本研究推出含16万余条目的专用数据集MedAlpaca用于微调LLM，并在医师资格考试类任务中验证了微调模型优于仅预训练模型的性能。
摘要：
随着OpenAI的GPT系列等大语言模型（LLM）不断取得进展，我们见证了人工智能应用在越来越广泛的领域中涌现。在医学领域，这些大语言模型在优化医疗工作流程、诊断、患者护理和教育方面具有巨大潜力。然而，目前迫切需要可在本地部署的开源模型，以保障患者隐私。在本研究中，我们提出了一个包含超过160,000条条目的创新数据集，专门用于微调大语言模型以实现有效的医疗应用。我们探究了在公开可用的预训练大语言模型上使用这些数据集进行微调的效果，随后，我们针对未来医生获得执业资格必须通过的考试，对比了仅预训练模型与微调后模型的性能。
英文摘要：
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving medical workflows, diagnostics, patient care, and education. Yet, there is an urgent need for open-source models that can be deployed on-premises to safeguard patient privacy. In our work, we present an innovative dataset consisting of over 160,000 entries, specifically crafted to fine-tune LLMs for effective medical applications. We investigate the impact of fine-tuning these datasets on publicly accessible pre-trained LLMs, and subsequently, we juxtapose the performance of pre-trained-only models against the fine-tuned models concerning the examinations that future medical doctors must pass to achieve certification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
