KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries

ArXi:2604.20859v1 Announce Type: cross Recent literature highlights the potential of graph-based approaches within large language model (LLM) retrieval-augmented generation (RAG) pipelines for answering queries of varying complexity, particularly those that fall outside the LLM's prior knowledge. However, LLMs are prone to hallucination and often face technical limitations in handling contexts large enough to ground complex queries effectively.