GROUNDEDKG-RAG: Grounded Knowledge Graph Index for Long-document Question Answering

arXiv:2604.04359v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus on RAG systems for long-document question answering. Current approaches suffer from a heavy reliance on LLM descriptions resulting in high resource consumption and latency, repetitive content across hierarchical