How can LLMs keep medical Q&A current?
Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
This paper introduces AMG-RAG, a system for medical question answering that combines a dynamically updated knowledge graph with retrieval augmented generation and chain-of-thought reasoning. It automatically builds and updates a medical knowledge graph, allowing the system to stay current with medical advances. AMG-RAG outperforms much larger language models on medical question-answering benchmarks without requiring fine-tuning, highlighting the potential of integrating structured knowledge and reasoning capabilities into LLM-based systems for enhanced accuracy and efficiency in specialized domains. While not explicitly a multi-agent system, AMG-RAG leverages multiple components (LLM agents for term extraction and relationship inference, search tools, knowledge graph database, and reasoning modules) working together in a pipeline, demonstrating a modular, agent-like approach to problem-solving that could be relevant for multi-agent LLM system design. The dynamic knowledge graph construction and update mechanism offers insights for managing evolving knowledge within multi-agent systems.