RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

ACL ARR 2025 May Submission6997 Authors

Abstract:

Medical question answering fundamentally relies on accurate clinical knowledge. The dominant paradigm, Retrieval-Augmented Generation (RAG), acquires expertise \textit{conceptual} knowledge from large-scale medical corpus to guide general-purpose large language models (LLMs) in generating trustworthy answers.
However, existing retrieval approaches often overlook the patient-specific \textit{factual knowledge} embedded in Electronic Health Records (EHRs), which limits the contextual relevance of retrieved \textit{conceptual knowledge} and hinders its effectiveness in vital clinical decision-making.
This paper introduces RGAR, a recurrence generation-augmented retrieval framework that synergistically retrieves both \textit{factual} and \textit{conceptual} knowledge from dual sources (i.e., EHRs and the corpus), allowing mutual refinement through iterative interaction.
Across three factual-aware medical QA benchmarks, RGAR establishes new state-of-the-art performance among medical RAG systems.
Notably, RGAR enables the Llama-3.1-8B-Instruct model to surpass the considerably larger GPT-3.5 augmented with traditional RAG.
Our findings demonstrate the benefit of explicitly mining patient-specific factual knowledge during retrieval, consistently improving generation quality and clinical relevance.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: biomedical QA, retrieval-augmented generation

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 6997