Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation

ArXi:2510.11541v2 Announce Type: replace Retrieval-augmented generation (RAG) has nstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval of multiple information targets.