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

• URL: http://arxiv.org/abs/2404.16130v1
• Date: Wed, 24 Apr 2024 18:38:11 GMT
• Title: From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• Authors: Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Jonathan Larson,
• Abstract summary: We propose a Graph RAG approach to question answering over private text corpora. Our approach uses an entity knowledge graph from the source documents, then to pregenerate community summaries for all groups of closely-related entities. For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG leads to substantial improvements over a na"ive RAG baseline.
• Score: 3.9676927113698626
• License: http://creativecommons.org/licenses/by/4.0/
• 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 the quantities of text indexed by typical RAG systems. To combine the strengths of these contrasting methods, we propose a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed. Our approach uses an LLM to build a graph-based text index in two stages: first to derive an entity knowledge graph from the source documents, then to pregenerate community summaries for all groups of closely-related entities. Given a question, each community summary is used to generate a partial response, before all partial responses are again summarized in a final response to the user. For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG leads to substantial improvements over a na\"ive RAG baseline for both the comprehensiveness and diversity of generated answers. An open-source, Python-based implementation of both global and local Graph RAG approaches is forthcoming at https://aka.ms/graphrag.

Related papers

• Generative Retrieval for Book search [106.67655212825025]
  We propose an effective Generative retrieval framework for Book Search. It features two main components: data augmentation and outline-oriented book encoding. Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-01-19T12:57:13Z)
• QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance [1.433758865948252]
  This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems. RAG architecture is constructed to generate responses from the target document. We introduce QuIM-RAG, a novel approach for the retrieval mechanism in our system.
  arXiv  Detail & Related papers  (2025-01-06T01:07:59Z)
• GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation [3.2027710059627545]
  We introduce Graphical Eigen Memories For Retrieval Augmented Generation (GEM-RAG) GEM-RAG works by tagging each chunk of text in a given text corpus with LLM generated utility'' questions. We evaluate GEM-RAG, using both UnifiedQA and GPT-3.5 Turbo as the LLMs, with SBERT, and OpenAI's text encoders on two standard QA tasks.
  arXiv  Detail & Related papers  (2024-09-23T21:42:47Z)
• Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation [65.16137964758612]
  We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Our objective is to test the capabilities of LLMs to analyze, understand, and reason over problems that require a detailed comprehension of long spans of text.
  arXiv  Detail & Related papers  (2024-05-31T20:15:10Z)
• TANQ: An open domain dataset of table answered questions [15.323690523538572]
  TANQ is the first open domain question answering dataset where the answers require building tables from information across multiple sources. We release the full source attribution for every cell in the resulting table and benchmark state-of-the-art language models in open, oracle, and closed book setups. Our best-performing baseline, GPT4 reaches an overall F1 score of 29.1, lagging behind human performance by 19.7 points.
  arXiv  Detail & Related papers  (2024-05-13T14:07:20Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph [0.0]
  We evaluate strategies for fine-tuning the OpenLlama LLM for question answering over life science knowledge graphs. We propose an end-to-end data augmentation approach for extending a set of existing queries over a given knowledge graph. We also investigate the role of semantic "clues" in the queries, such as meaningful variable names and inline comments.
  arXiv  Detail & Related papers  (2024-02-07T07:24:01Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• LMGQS: A Large-scale Dataset for Query-focused Summarization [77.6179359525065]
  We convert four generic summarization benchmarks into a new QFS benchmark dataset, LMGQS. We establish baselines with state-of-the-art summarization models. We achieve state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks.
  arXiv  Detail & Related papers  (2023-05-22T14:53:45Z)
• Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph [34.774049199809426]
  Knowledge graph question answering (i.e., KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. We first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm. We then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them.
  arXiv  Detail & Related papers  (2021-11-20T08:27:37Z)
• Semantic Graphs for Generating Deep Questions [98.5161888878238]
  We propose a novel framework which first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN) On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-27T10:52:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.