From Local to Global: A Graph RAG Approach to Query-Focused Summarization

arXiv:2404.16130v1 Announce Type: new
Abstract: The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text corpus, such as "What are the main themes in the dataset?", since this is inherently a query-focused summarization (QFS) task, rather than an explicit retrieval task. Prior QFS methods, meanwhile, fail to scale to …

arxiv cs.ai cs.cl cs.ir global graph query rag summarization type