A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
Summary: arXiv:2604.07274v1 Announce Type: cross
Large language models (LLMs) have shown remarkable capabilities in the field of medical question answering. However, it has been observed that purely parametric models often face issues related to knowledge gaps and limited factual grounding. To address these limitations, retrieval-augmented generation (RAG) has emerged as a promising approach by integrating external knowledge retrieval into the reasoning process of LLMs. Despite the growing interest in RAG-based medical systems, the specific impact of individual retrieval components on overall performance remains poorly understood.
This study presents a systematic evaluation of retrieval-augmented medical question answering by utilizing the MedQA USMLE benchmark along with a structured textbook-based knowledge corpus. The researchers conducted an in-depth analysis of various factors, including:
- Language models
- Embedding models
- Retrieval strategies
- Query reformulation
- Cross-encoder reranking
All these components were examined within a unified experimental framework that comprised a total of forty different configurations. The findings of the study indicate that retrieval augmentation significantly enhances the performance of zero-shot medical question answering. Among the configurations tested, the best-performing setup involved dense retrieval coupled with query reformulation and reranking, achieving an impressive accuracy of 60.49%.
Furthermore, the research highlighted that domain-specialized language models exhibited a greater ability to effectively utilize retrieved medical evidence compared to general-purpose models. This insight underscores the importance of tailoring language models for specific domains to optimize their performance in specialized tasks.
Another critical aspect revealed in the analysis is the tradeoff between retrieval effectiveness and computational cost. The results suggest that simpler dense retrieval configurations can deliver strong performance while also maintaining higher throughput. This finding is particularly noteworthy as it demonstrates that effective retrieval-augmented medical question answering systems can be developed and evaluated using modest computational resources. All experiments were conducted on a single consumer-grade GPU, indicating that researchers and practitioners in the field can leverage these findings without the need for extensive computational infrastructure.
In conclusion, this systematic study not only sheds light on the various components that contribute to the effectiveness of retrieval-augmented medical question answering but also emphasizes the feasibility of conducting such evaluations with limited resources. The insights gained from this research can pave the way for future advancements in medical AI systems, ultimately enhancing their ability to provide accurate and reliable answers to medical inquiries.