Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare (2404.16621v1)

Abstract: The integration of 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.

Exploring the Open-Source Hippocrates Framework for Enhancing Medical LLMs

Introduction

The Hippocrates paper introduces an open-source LLM framework specifically designed for the medical domain, named after the ancient Greek physician. The framework not only provides a comprehensive, transparent access to training datasets, codebases, checkpoints, and evaluation protocols but also introduces a family of models, nicknamed Hippo, optimized for medical tasks. These models, derived from continuations of the pre-trained models Mistral and LLaMA2, showcase significant performance gains in medical benchmarks, surpassing both generic large models and other specialized counterparts up to 70B parameters.

The Hippocrates Framework and Its Components

Pre-training and Fine-Tuning

The Hippocrates models undergo a rigorous training regime, beginning with continued pre-training on a select compilation of medical texts comprising Medical Guidelines, PMC-Patients, and PubMedQA-contexts, amounting to approximately 300 million tokens. This is followed by instruction tuning, leveraging a novel dual-set approach of both General Instructions and Evaluation Instructions Data. This structured approach is critical in refining the model's ability to process and comprehend complex medical language and scenarios.

Integration of Medical Preferences

Unique to the Hippocrates framework is the integration of medical preferences through reinforcement learning from AI and human feedback, using an innovative strategy called Medical RLAIF, which supports the alignment of model outcomes with professional medical judgments at reduced costs compared to traditional methods.

Training Methodology

Hippocrates employs advanced techniques such as Low-Rank Adaptation (LoRA) to optimize training efficiency. The models are incrementally improved through a multi-stage training process, starting with domain-specific pre-training, moving into supervised fine-tuning with tailored instruction data, and finally preference learning directly from domain expert feedback.

Performance and Evaluation

Benchmarking Success

In terms of performance, the Hippo models not only show superior capabilities in zero-shot and few-shot settings but also demonstrate significant improvements over state-of-the-art models with substantially higher parameter counts. The models were evaluated across several medical question-answering benchmarks including MedMCQA, PubMedQA, and the USMLE steps 1-3, leveraging the EleutherAI evaluation framework.

Comparative Analysis

The evaluation highlights not only the raw performance improvements but also offers insights into the specific contributions of each training phase. The step-by-step approach shows clear incremental benefits, particularly the addition of instruction tuning and preference learning which notably refine the models' output to align better with real-world medical decision-making.

Conclusions and Future Implications

The Hippocrates framework sets a new standard for transparency and performance in medical LLMs. By providing open access to all elements of the training and evaluation process, it fosters a collaborative research environment and significantly lowers the barriers to entry for academic investigations in this field.

In the future, extensions of this framework could explore more diverse medical sub-domains, integrate multilingual support, or adapt the core methodologies to other specialized fields beyond healthcare. The ongoing development and rigorous evaluation of such models will be crucial in realizing the practical deployment of LLMs in sensitive areas like medical diagnostics and patient care, ensuring they are both effective and aligned with professional standards and ethics.

The release of the Hippocrates models represents a significant step forward in the journey towards more reliable, understandable, and accessible AI-driven tools in healthcare, promising not only to enhance patient care but also to democratize the benefits of AI applications in medicine globally.