Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models

• URL: http://arxiv.org/abs/2406.12182v1
• Date: Tue, 18 Jun 2024 01:30:07 GMT
• Title: Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models
• Authors: Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua Zhou, Donglin Hao, Yonghua Lin,
• Abstract summary: We propose Aquila-Med, a bilingual medical LLM based on Aquila. We construct a large-scale Chinese and English medical dataset for continue pre-training and a high-quality SFT dataset. Aquila-Med achieves notable results across single-turn, multi-turn dialogues, and medical multiple-choice questions.
• Score: 8.252044870864523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific professional fields such as medicine, especially within the open-source community, remains suboptimal due to the complexity of medical knowledge. We propose Aquila-Med, a bilingual medical LLM based on Aquila, addressing these challenges through continue pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). We construct a large-scale Chinese and English medical dataset for continue pre-training and a high-quality SFT dataset, covering extensive medical specialties. Additionally, we develop a high-quality Direct Preference Optimization (DPO) dataset for further alignment. Aquila-Med achieves notable results across single-turn, multi-turn dialogues, and medical multiple-choice questions, demonstrating the effectiveness of our approach. We open-source the datasets and the entire training process, contributing valuable resources to the research community. Our models and datasets will released at https://huggingface.co/BAAI/AquilaMed-RL.

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