Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering
arXiv:2606.25338v1 Announce Type: new Abstract: Large language models (LLMs) have shown promising performance across a wide range of biomedical applications, including medical question answering (QA), yet they remain prone to hallucinations and outdated knowledge. Although retrieval-augmented generation (RAG) can alleviate this issue by incorporating external documents, there still exist two fundamental limitations. First, medical knowledge is often fragmented across documents, while most RAG methods rely on a single retrieval path, which makes it challenging to jointly preserve fine-grained s
The proliferation of LLMs in critical applications like medical QA necessitates advanced retrieval strategies to mitigate inherent limitations such as hallucinations and outdated knowledge.
Improving the accuracy and reliability of LLMs in specialized domains like medicine is crucial for their adoption and impact, directly addressing safety and trust concerns.
This research introduces a more sophisticated RAG approach, 'Hybrid-IR', that moves beyond single-path retrieval, promising more robust and accurate complex medical question answering.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · AI research community
- · LLMs without advanced RAG
- · Traditional, static knowledge bases
Reduced hallucination rates and improved factual accuracy in medical LLM applications.
Increased trust and accelerated adoption of AI in clinical decision support and diagnostics.
Potential for new regulatory frameworks and industry standards for AI reliability in healthcare.
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Read at arXiv cs.CL