GeneRAG: Enhancing Large Language Models with Gene-Related Task by Retrieval-Augmented Generation

Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing and are used in gene analysis, but their gene knowledge is incomplete. Fine-tuning LLMs with external data is costly and resource-intensive. Retrieval-Augmented Generation (RAG) integrates relevant external information dynamically. We introduce G ene RAG, a frame-work that enhances LLMs’ gene-related capabilities using RAG and the Maximal Marginal Relevance (MMR) algorithm. Evaluations with datasets from the National Center for Biotechnology Information (NCBI) show that G ene RAG outperforms GPT-3.5 and GPT-4, with a 39% improvement in answering gene questions, a 43% performance increase in cell type annotation, and a 0.25 decrease in error rates for gene interaction prediction. These results highlight G ene RAG’s potential to bridge a critical gap in LLM capabilities for more effective applications in genetics.