BiomedRAG: A retrieval augmented large language model for biomedicine

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…

• NI
  National Institutes of Health
• NI
  National Institute on Aging

  Award: R01AG078154
• NC
  National Cancer Institute

  Award: R01CA287413
• NC
  National Center for Complementary and Integrative Health

  Award: R01AT009457