Augmenting Black-box LLM s with Medical Textbooks for Biomedical Question Answering (2024)

Abstract: Large-scale language models (LLMs) like ChatGPT have demonstrated impressive abilities in generating responses based on human instructions. However, their use in the medical field can be challenging due to their lack of specific, in-depth knowledge. In this study, we present a system called LLMs Augmented with Medical Textbooks (LLM-AMT) designed to enhance the proficiency of LLMs in specialized domains. LLM-AMT integrates authoritative medical textbooks into the LLMs’ framework using plug-and-play modules. These modules include a Query Augmenter, a Hybrid Textbook Retriever, and a Knowledge Self-Refiner. Together, they incorporate authoritative medical knowledge. Additionally, an LLM Reader aids in contextual understanding. Our experimental results on three medical QA tasks demonstrate that LLM-AMT significantly improves response quality, with accuracy gains ranging from 11.6% to 16.6%. Notably, with GPT-4-Turbo as the base model, LLM-AMT outperforms the specialized Med-PaLM 2 model pre-trained on a massive amount of medical corpus by 2-3%. We found that despite being 100 smaller in size, medical textbooks as a retrieval corpus are proven to be a more effective knowledge database than Wikipedia in the medical domain, boosting performance by 7.8%-13.7%.

## introduction recent advancements in large language models (llms) have opened new possibilities in ai for the medical domain, enabling them to comprehend and communicate through language. the promise of these models is underscored by their performance on medical question-answering datasets @xcite @xcite . llms are typically trained to encode world knowledge in their parameters. however, this can lead to information loss and "memory distortion" @xcite , resulting in the generation of plausible but incorrect content. augmenting llms with external knowledge has become an interest to mitigate this, but fine-tuning llm parameters for this purpose is often costly, especially as model sizes increase @xcite @xcite . the retrieval-augmented generation (rag) framework provides an efficient solution to the limitations of fine-tuning in open-domain qa, pairing a retriever for sourcing relevant documents with a reader for answer extraction @xcite @xcite . enhancements in retrieval accuracy @xcite and reader model co-training @xcite have been made, with current iterations leveraging llms as readers to adapt specifically to their capabilities @xcite . however, many rely on general knowledge bases like wikipedia or search engines such as google and bing. such sources, while vast, might lack depth in domain-specific areas like medical or financial fields. tapping into specialized resources, such as authoritative textbooks, could yield deeper insights into complex domains. the effectiveness of the retrieval process in enhancing llms with additional information is heavily reliant on the quality of retrieval. if the retrieval process is inaccurate or contains misinformation, the utility of the rag process can be significantly influenced @xcite @xcite . to address these challenges, several approaches have been proposed, such as hyde @xcite and query2doc @xcite , which aim to improve retrieval by generating hypothetical documents to expand the query. on the other hand, methods like self-rag @xcite have introduced retrieval results reflection to filter the retrieved information for better generation. building on these advancements, we have developed query augmenter and knowledge self-refiner specifically tailored for the medical rag paradigm. combining with hybrid textbook retriever, we have created a novel framework, llm-amt, specifically designed for the medical domain. this framework leverages medical textbooks to provide llms with high-quality, reliable knowledge. this approach ensures that the information used during the generation process is not only accurate but also highly relevant to answer the questions. our evaluations across medqa-usmle, medqa-mcmle, and medmcqa datasets demonstrate llm-amt's outperformance over gpt-3.5, achieving 11.6% to 16.6% higher accuracy. notably, our approach leverages the in-depth knowledge from textbooks, eclipsing wikipedia's broader scope and a 7.8% to 13.7% accuracy gain. with gpt-4-turbo as a base, llm-amt further exceeds the medically pre-trained med-palm 2 by 2.3% to 2.7%. additionally, human evaluations reveal a significant 16% reduction in hallucination occurrences during open-ended qa tasks, showcasing the model's improved reliability. our contributions are fourfold: ## llm-amt in this paper, we introduce llm-amt, a dedicated process tailored for answering biomedical questions. figure 1 provides an overview of the process pipeline, which consists of four main components. the query augmenter rewrites and expands the input question x into a rewritten version x r and an expanded version x e . following this, the textbook retriever collects related passages p 1 , p 2 , . . . , p k from textbooks by concatenating the augmented queries x r and x e . next, the knowledge self-refiner employs a relevance filter to remove nonpertinent passages and then applies a usefulness filter to discard unhelpful segments from the remaining passages, producing refined knowledge p . finally, the llm reader utilizes this refined knowledge to construct the final answer. ## related work in this section, we provide an overview of the related work in biomedical qa, retrieval-augmented qa, and text retrieval. ## conclusion we introduced llm-amt, a novel pipeline optimized for medical tasks, harnessing authoritative medical textbooks to enhance llms' accuracy and professionalism. empirical evaluations reinforced the value of integrating domain-specific textbooks with llms, providing an avenue for future studies. further, our ablation study delineated the significance of external knowledge retrieval, query augmentation, and knowledge self-refinement strategy within our proposed architecture. these findings set a precedent for advancing specialized domainaware models, especially in the context of medical informatics and healthcare ai applications.