Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910412831719424 |
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| author | Ye, Qichen Liu, Junling Chong, Dading Zhou, Peilin Hua, Yining Liu, Fenglin Cao, Meng Wang, Ziming Cheng, Xuxin Lei, Zhu Guo, Zhenhua |
| author_facet | Ye, Qichen Liu, Junling Chong, Dading Zhou, Peilin Hua, Yining Liu, Fenglin Cao, Meng Wang, Ziming Cheng, Xuxin Lei, Zhu Guo, Zhenhua |
| contents | Integrating large language models (LLMs) into healthcare holds great potential but faces challenges. Pre-training LLMs from scratch for domains like medicine is resource-heavy and often unfeasible. On the other hand, sole reliance on Supervised Fine-tuning (SFT) can result in overconfident predictions and may not tap into domain-specific insights. In response, we present a multi-stage training method combining Domain-specific Continued Pre-training (DCPT), SFT, and Direct Preference Optimization (DPO). In addition, we publish a 3Gb Chinese Medicine (ChiMed) dataset, encompassing medical question answering, plain texts, knowledge graphs, and dialogues, segmented into three training stages. The medical LLM trained with our pipeline, Qilin-Med, shows substantial performance improvement. In the CPT and SFT phases, Qilin-Med achieved 38.4% and 40.0% accuracy on the CMExam test set, respectively. It outperformed the basemodel Baichuan-7B (accuracy: 33.5%), by 7.5%. In the DPO phase, it scored 16.66 in BLEU-1 and 27.44 in ROUGE-1 on the Huatuo-26M test set, bringing further improvement to the SFT phase (12.69 in BLEU-1 and 24.21 in ROUGE-1). Additionally, we have further enhanced the model's performance through the Retrieval Augmented Generation (RAG) approach. Experiments demonstrate that Qilin-Med-RAG achieves an accuracy rate of 42.8% on CMExam. These results highlight the contribution of our novel training approach in building LLMs for medical applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_09089 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model Ye, Qichen Liu, Junling Chong, Dading Zhou, Peilin Hua, Yining Liu, Fenglin Cao, Meng Wang, Ziming Cheng, Xuxin Lei, Zhu Guo, Zhenhua Computation and Language Integrating large language models (LLMs) into healthcare holds great potential but faces challenges. Pre-training LLMs from scratch for domains like medicine is resource-heavy and often unfeasible. On the other hand, sole reliance on Supervised Fine-tuning (SFT) can result in overconfident predictions and may not tap into domain-specific insights. In response, we present a multi-stage training method combining Domain-specific Continued Pre-training (DCPT), SFT, and Direct Preference Optimization (DPO). In addition, we publish a 3Gb Chinese Medicine (ChiMed) dataset, encompassing medical question answering, plain texts, knowledge graphs, and dialogues, segmented into three training stages. The medical LLM trained with our pipeline, Qilin-Med, shows substantial performance improvement. In the CPT and SFT phases, Qilin-Med achieved 38.4% and 40.0% accuracy on the CMExam test set, respectively. It outperformed the basemodel Baichuan-7B (accuracy: 33.5%), by 7.5%. In the DPO phase, it scored 16.66 in BLEU-1 and 27.44 in ROUGE-1 on the Huatuo-26M test set, bringing further improvement to the SFT phase (12.69 in BLEU-1 and 24.21 in ROUGE-1). Additionally, we have further enhanced the model's performance through the Retrieval Augmented Generation (RAG) approach. Experiments demonstrate that Qilin-Med-RAG achieves an accuracy rate of 42.8% on CMExam. These results highlight the contribution of our novel training approach in building LLMs for medical applications. |
| title | Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2310.09089 |