DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation

• URL: http://arxiv.org/abs/2308.14346v1
• Date: Mon, 28 Aug 2023 06:41:49 GMT
• Title: DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
• Authors: Zhijie Bao, Wei Chen, Shengze Xiao, Kuang Ren, Jiaao Wu, Cheng Zhong, Jiajie Peng, Xuanjing Huang, Zhongyu Wei
• Abstract summary: We propose DISC-MedLLM to provide accurate and truthful medical response in end-to-end conversational healthcare services. We employ three strategies: utilizing medical knowledge-graphs, reconstructing real-world dialogues, and incorporating human-guided preference rephrasing.
• Score: 37.08249140671163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose DISC-MedLLM, a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthcare services. To construct high-quality Supervised Fine-Tuning (SFT) datasets, we employ three strategies: utilizing medical knowledge-graphs, reconstructing real-world dialogues, and incorporating human-guided preference rephrasing. These datasets are instrumental in training DISC-MedLLM, surpassing existing medical LLMs in both single-turn and multi-turn consultation scenarios. Extensive experimental results demonstrate the effectiveness of the proposed model in bridging the gap between general language models and real-world medical consultation. Additionally, we release the constructed dataset and model weights to further contribute to research and development. Further details and resources can be found at https://github.com/FudanDISC/DISC-MedLLM

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