Knowledge Enhancement of Small-Scale Models in Medical Question Answering (Preprint)
Authors: Li Xinbai , Peng Shaowen , Wakamiya Shoko , Aramaki Eiji 
Published: November 2024

BACKGROUND

Medical question answering (QA) is essential for various medical applications. While small-scale pre-training language models (PLMs) are widely adopted in open-domain QA tasks through fine-tuning with related datasets, applying this approach in the medical domain requires significant and rigorous integration of external knowledge. Knowledge-enhanced small-scale PLMs have been proposed to incorporate knowledge bases (KBs) to improve performance, as KBs contain vast amounts of factual knowledge. Large language models (LLMs) contain a vast amount of knowledge and have attracted significant research interest due to their outstanding natural language processing (NLP) capabilities. KBs and LLMs can provide external knowledge to enhance small-scale models in medical QA.

OBJECTIVE

KBs consist of structured factual knowledge that must be converted into sentences to align with the input format of PLMs. However, these converted sentences often lack semantic coherence, potentially causing them to deviate from the intrinsic knowledge of KBs. LLMs, on the other hand, can generate natural, semantically rich sentences, but they may also produce irrelevant or inaccurate statements. Retrieval-augmented generation (RAG) paradigm enhances LLMs by retrieving relevant information from an external database before responding. By integrating LLMs and KBs using the RAG paradigm, it is possible to generate statements that combine the factual knowledge of KBs with the semantic richness of LLMs, thereby enhancing the performance of small-scale models. In this paper, we explore a RAG fine-tuning method, RAG-mQA, that combines KBs and LLMs to improve small-scale models in medical QA.

METHODS

In the RAG fine-tuning scenario, we adopt medical KBs as an external database to augment the text generation of LLMs, producing statements that integrate medical domain knowledge with semantic knowledge. Specifically, KBs are used to extract medical concepts from the input text, while LLMs are tasked with generating statements based on these extracted concepts. In addition, we introduce two strategies for constructing knowledge: KB-based and LLM-based construction. In the KB-based scenario, we extract medical concepts from the input text using KBs and convert them into sentences by connecting the concepts sequentially. In the LLM-based scenario, we provide the input text to an LLM, which generates relevant statements to answer the question. For downstream QA tasks, the knowledge produced by these three strategies is inserted into the input text to fine-tune a small-scale PLM. F1 and exact match (EM) scores are employed as evaluation metrics for performance comparison. Fine-tuned PLMs without knowledge insertion serve as baselines. Experiments are conducted on two medical QA datasets: emrQA (English) and MedicalQA (Chinese).

RESULTS

RAG-mQA achieved the best results on both datasets. On the MedicalQA dataset, compared to the KB-based and LLM-based enhancement methods, RAG-mQA improved the F1 score by 0.59% and 2.36%, and the EM score by 2.96% and 11.18%, respectively. On the emrQA dataset, the EM score of RAG-mQA exceeded those of the KB-based and LLM-based methods by 4.65% and 7.01%, respectively.

CONCLUSIONS

Experimental results demonstrate that RAG fine-tuning method can improve the model performance in medical QA. RAG-mQA achieves greater improvements compared to other knowledge-enhanced methods.

CLINICALTRIAL

This study does not involve trial registration.