Graph-R1: An Agentic GraphRAG Framework for Structured, Multi-Turn Reasoning with Reinforcement Learning
Introduction Large Language Models (LLMs) have set new benchmarks in natural language processing, but their tendency for hallucination—generating inaccurate outputs—remains a critical issue for knowledge-intensive applications. Retrieval-Augmented Generation (RAG) frameworks attempt to solve this by incorporating external knowledge into language generation. However, traditional RAG approaches rely on chunk-based retrieval, which limits their ability to represent…
