Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering

• URL: http://arxiv.org/abs/2311.00204v1
• Date: Wed, 1 Nov 2023 00:18:00 GMT
• Title: Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering
• Authors: Zhen Guo, Yining Hua
• Abstract summary: Large language models exhibit promising general capabilities but often lack specialized knowledge for domain-specific tasks. This work demonstrates a method using continuous training and instruction fine-tuning to rapidly adapt Llama 2 base models to the Chinese medical domain.
• Score: 4.254954312483959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models exhibit promising general capabilities but often lack specialized knowledge for domain-specific tasks. Developing domain experts from a base model enables a range of applications without prohibitive training costs. This work demonstrates a method using continuous training and instruction fine-tuning to rapidly adapt Llama 2 base models to the Chinese medical domain. We first conduct continuous training on 1B tokens from Chinese medical references to teach relevant vocabulary and knowledge. The models are then fine-tuned on 54K examples sourced from the Chinese National Medical Licensing Examination. Experiments on Chinese medical data confirm the effectiveness of this approach, producing a model comparable to GPT-3.5-turbo while using way less computational resource. The resulting domain-specific model could be useful for various Chinese medical applications. More broadly, this provides a template for domain-specific training of large language models in areas where pre-trained models lack the required expertise, such as law, science, and engineering.

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