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  11. Biomedical Knowledge Graph-optimized Prompt Generation For Large Language Models

Biomedical knowledge graph-optimized prompt generation for large language models

Karthik Soman1, Peter W Rose2, John H Morris3

  • 1Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, United States.

Bioinformatics (Oxford, England)
|September 17, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a token-optimized Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework. KG-RAG significantly improves large language model performance on biomedical tasks with reduced token consumption and enhanced accuracy.

Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence

Background:

  • Large language models (LLMs) face challenges in knowledge-intensive domains like biomedicine.
  • Current solutions like pretraining and fine-tuning incur high computational costs and require domain expertise.

Purpose of the Study:

  • To introduce a token-optimized Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework.
  • To leverage the SPOKE biomedical knowledge graph (KG) with LLMs for generating accurate biomedical text.

Main Methods:

  • Developed a KG-RAG framework utilizing the SPOKE KG and LLMs (Llama-2, GPT-3.5, GPT-4).
  • Implemented context extraction with minimal graph schema and context pruning via embedding methods.
  • Optimized token consumption for cost-effective RAG on proprietary LLMs.

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Main Results:

  • Achieved over 50% reduction in token consumption without compromising accuracy.
  • Enhanced LLM performance across diverse biomedical prompts, providing provenance and statistical evidence.
  • Boosted Llama-2 model performance by 71% on a challenging multiple-choice question dataset.

Conclusions:

  • The KG-RAG framework effectively combines explicit KG knowledge and implicit LLM knowledge in a token-optimized manner.
  • This approach enhances the adaptability of general-purpose LLMs for domain-specific biomedical questions cost-effectively.
  • The framework empowers open-source models for specialized tasks and improves proprietary LLM performance.