BiomedRAG: A retrieval augmented large language model for biomedicine

Abstract

Retrieval-augmented generation (RAG) involves a solution by retrieving knowledge from an established database to enhance the performance of large language models (LLM). , these models retrieve information at the sentence or paragraph level, potentially introducing noise and affecting the generation quality. To address these issues, we propose a novel BiomedRAG framework that directly feeds automatically retrieved chunk-based documents into the LLM. Our evaluation of BiomedRAG across four biomedical natural language processing tasks using eight datasets demonstrates that our proposed framework not only improves the performance by 9.95% on average, but also achieves state-of-the-art results, surpassing various baselines by 4.97%. BiomedRAG paves the way for more accurate and adaptable LLM applications in the biomedical domain. • We proposed BiomedRAG , a novel chunk retrieval framework for biomedical NLP tasks. • We used the LLM scores as a supervision signal to train the proposed chunk scorer. • We validated it on four biomedical NLP tasks on eight datasets using various LLMs.

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