Hippocrates：一个推动医疗领域大型语言模型发展的开源框架
Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare
AI总结：
提出开源医疗LLM框架Hippocrates，提供数据集、代码、检查点和评估协议的完全访问，并推出基于Mistral和LLaMA2微调的7B模型Hippo，性能超越现有开源医疗LLM甚至70B模型。
摘要：
将大型语言模型（LLMs）整合到医疗领域有望变革医学诊断、研究和患者护理。然而，医疗LLMs的发展面临着诸多障碍，如复杂的训练要求、严格的评估需求，以及专有模型的主导地位限制了学术探索。透明、全面地获取LLM资源对于推动该领域发展、促进可复现性以及鼓励医疗AI创新至关重要。我们提出了Hippocrates，一个专门为医疗领域开发的开源LLM框架。与以往的努力形成鲜明对比的是，它提供了对其训练数据集、代码库、检查点和评估协议的无限制访问。这种开放方法旨在激发协作研究，使社区能够在透明生态系统中构建、完善和严格评估医疗LLMs。此外，我们引入了Hippo，一系列专为医疗领域定制的7B模型，通过持续预训练、指令微调以及来自人类和AI反馈的强化学习，从Mistral和LLaMA2微调而来。我们的模型以巨大优势超越了现有的开源医疗LLMs，甚至超过了拥有70B参数的模型。通过Hippocrates，我们期望释放LLMs的全部潜力，不仅推动医学知识和患者护理的进步，还使医疗AI研究的益处民主化，在全球范围内普及。
英文摘要：
The integration of Large Language Models (LLMs) into healthcare promises to transform medical diagnostics, research, and patient care. Yet, the progression of medical LLMs faces obstacles such as complex training requirements, rigorous evaluation demands, and the dominance of proprietary models that restrict academic exploration. Transparent, comprehensive access to LLM resources is essential for advancing the field, fostering reproducibility, and encouraging innovation in healthcare AI. We present Hippocrates, an open-source LLM framework specifically developed for the medical domain. In stark contrast to previous efforts, it offers unrestricted access to its training datasets, codebase, checkpoints, and evaluation protocols. This open approach is designed to stimulate collaborative research, allowing the community to build upon, refine, and rigorously evaluate medical LLMs within a transparent ecosystem. Also, we introduce Hippo, a family of 7B models tailored for the medical domain, fine-tuned from Mistral and LLaMA2 through continual pre-training, instruction tuning, and reinforcement learning from human and AI feedback. Our models outperform existing open medical LLMs models by a large-margin, even surpassing models with 70B parameters. Through Hippocrates, we aspire to unlock the full potential of LLMs not just to advance medical knowledge and patient care but also to democratize the benefits of AI research in healthcare, making them available across the globe.
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。
