Large-scale language models (LLMs), such as ChatGPT, are capable of
generating human-like responses for various downstream tasks, such as
task-oriented dialogues and question answering. However, applying LLMs to
medical domains remains challenging due to their inability to leverage
domain-specific knowledge. In this study, we present the Large-scale Language
Models Augmented with Medical Textbooks (LLM-AMT), which integrates
authoritative medical textbooks as the cornerstone of its design, enhancing its
proficiency in the specialized domain through plug-and-play modules, comprised
of a Hybrid Textbook Retriever, supplemented by the Query Augmenter and the LLM
Reader. Experimental evaluation on three open-domain medical question-answering
tasks reveals a substantial enhancement in both the professionalism and
accuracy of the LLM responses when utilizing LLM-AMT, exhibiting an improvement
ranging from 11.4% to 13.2%. Despite being 100 times smaller, we found that
medical textbooks as the retrieval corpus serves as a more valuable external
knowledge source than Wikipedia in the medical domain. Our experiments show
that textbook augmentation results in a performance improvement ranging from
9.7% to 12.2% over Wikipedia augmentation.

医学文本书籍作为检索语料库在医疗领域比维基百科更具价值，该研究提出了将医学文本书籍用于增强大规模语言模型（LLMs），并通过插拔式模块集成了医学专业领域的专业知识，以提高 LLMs 在医学相关任务中的专业性和准确性。实验结果显示，使用 LLM-AMT 相比使用维基百科，LLMs 的回答在专业性和准确性上均有显著提高。