  Recently, large language model (LLM) based artificial intelligence (AI)
systems have demonstrated remarkable capabilities in natural language
understanding and generation. However, these models face a significant
challenge when it comes to sensitive applications, such as reasoning over
medical knowledge and answering medical questions in a physician-like manner.
Prior studies attempted to overcome this challenge by increasing the model size
(>100B) to learn more general medical knowledge, while there is still room for
improvement in LLMs with smaller-scale model sizes (<100B). In this work, we
start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a
medical beginner towards a medical expert (called AntGLM-Med-10B), which
leverages a 3-stage optimization procedure, i.e., general medical knowledge
injection, medical domain instruction tuning, and specific medical task
adaptation. Our contributions are threefold: (1) We specifically investigate
how to adapt a pre-trained general LLM in medical domain, especially for a
specific medical task. (2) We collect and construct large-scale medical
datasets for each stage of the optimization process. These datasets encompass
various data types and tasks, such as question-answering, medical reasoning,
multi-choice questions, and medical conversations. (3) Specifically for
multi-choice questions in the medical domain, we propose a novel
Verification-of-Choice approach for prompting engineering, which significantly
enhances the reasoning ability of LLMs. Remarkably, by combining the above
approaches, our AntGLM-Med-10B model can outperform the most of LLMs on
PubMedQA, including both general and medical LLMs, even when these LLMs have
larger model size.
