GeAR: Graph-enhanced Agent for Retrieval-augmented Generation

• URL: http://arxiv.org/abs/2412.18431v2
• Date: Sun, 22 Jun 2025 12:13:24 GMT
• Title: GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
• Authors: Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, YE QI, Yang Ren, Dandan Tu, Jeff Z. Pan,
• Abstract summary: Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities.<n>Traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios.<n>We introduce GeAR, a system that advances RAG performance through two key innovations.
• Score: 12.966494167631113
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce GeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates GeAR's superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io.

Related papers

• FrugalRAG: Learning to retrieve and reason for multi-hop QA [10.193015391271535]
  Large-scale fine-tuning is not needed to improve RAG metrics.<n>Supervised and RL-based fine-tuning can help RAG from the perspective of frugality.
  arXiv  Detail & Related papers  (2025-07-10T11:02:13Z)
• ImpRAG: Retrieval-Augmented Generation with Implicit Queries [49.510101132093396]
  ImpRAG is a query-free RAG system that integrates retrieval and generation into a unified model.<n>We show that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats.
  arXiv  Detail & Related papers  (2025-06-02T21:38:21Z)
• Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing [4.874077691069634]
  Retrieval Augmented Generation (RAG) has shown strong capability in enhancing language models' knowledge and reducing AI generative hallucinations.<n>Current multi-round RAG systems may continue searching even when enough information has already been retrieved.<n>This paper introduces a new framework, SIM-RAG, to explicitly enhance RAG systems' self-awareness and multi-round retrieval capabilities.
  arXiv  Detail & Related papers  (2025-05-05T17:39:35Z)
• Reasoning of Large Language Models over Knowledge Graphs with Super-Relations [53.14275361052276]
  We propose the ReKnoS framework, which aims to Reason over Knowledge Graphs with Super-Relations. Our framework's key advantages include the inclusion of multiple relation paths through super-relations. The results demonstrate the superior performance of ReKnoS over existing state-of-the-art baselines, with an average accuracy gain of 2.92%.
  arXiv  Detail & Related papers  (2025-03-28T06:11:04Z)
• RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
  We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline. RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components. Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
  arXiv  Detail & Related papers  (2025-03-25T03:21:48Z)
• MultiConIR: Towards multi-condition Information Retrieval [57.6405602406446]
  We introduce MultiConIR, the first benchmark designed to evaluate retrieval models in multi-condition scenarios. We propose three tasks to assess retrieval and reranking models on multi-condition robustness, monotonic relevance ranking, and query format sensitivity.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation [30.485127201645437]
  We propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA.
  arXiv  Detail & Related papers  (2025-02-25T17:47:53Z)
• ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents [27.90338725230132]
  ViDoSeek is a dataset designed to evaluate RAG performance on visually rich documents requiring complex reasoning. We propose ViDoRAG, a novel multi-agent RAG framework tailored for complex reasoning across visual documents. Notably, ViDoRAG outperforms existing methods by over 10% on the competitive ViDoSeek benchmark.
  arXiv  Detail & Related papers  (2025-02-25T09:26:12Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• SiReRAG: Indexing Similar and Related Information for Multihop Reasoning [96.60045548116584]
  SiReRAG is a novel RAG indexing approach that explicitly considers both similar and related information.<n>SiReRAG consistently outperforms state-of-the-art indexing methods on three multihop datasets.
  arXiv  Detail & Related papers  (2024-12-09T04:56:43Z)
• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval [103.116634967815]
  We introduce CodeXEmbed, a family of large-scale code embedding models ranging from 400M to 7B parameters. Our novel training pipeline unifies multiple programming languages and transforms various code-related tasks into a common retrieval framework. Our 7B model sets a new state-of-the-art (SOTA) in code retrieval, outperforming the previous leading model, Voyage-Code, by over 20% on CoIR benchmark.
  arXiv  Detail & Related papers  (2024-11-19T16:54:45Z)
• xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token [108.7069350303884]
  xRAG is an innovative context compression method tailored for retrieval-augmented generation.<n>xRAG seamlessly integrates document embeddings into the language model representation space.<n> Experimental results demonstrate that xRAG achieves an average improvement of over 10% across six knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2024-05-22T16:15:17Z)
• RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation [42.82192656794179]
  Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process.
  arXiv  Detail & Related papers  (2024-03-31T08:58:54Z)
• Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers [0.0]
  Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q&A (Question-Answering) systems. We propose the 'Blended RAG' method of leveraging semantic search techniques, such as Vector indexes and Sparse indexes, blended with hybrid query strategies. Our study achieves better retrieval results and sets new benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID datasets.
  arXiv  Detail & Related papers  (2024-03-22T17:13:46Z)
• RAGGED: Towards Informed Design of Scalable and Stable RAG Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge.<n>RAGGED is a framework for systematically evaluating RAG systems.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• Retrieval-Augmented Generation for AI-Generated Content: A Survey [38.50754568320154]
  Retrieval-Augmented Generation (RAG) has emerged as a paradigm to address such challenges. RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios.
  arXiv  Detail & Related papers  (2024-02-29T18:59:01Z)
• Distillation Enhanced Generative Retrieval [96.69326099136289]
  Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods.
  arXiv  Detail & Related papers  (2024-02-16T15:48:24Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)
• SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval [92.27387459751309]
  We provide SPRINT, a unified Python toolkit for evaluating neural sparse retrieval. We establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document.
  arXiv  Detail & Related papers  (2023-07-19T22:48:02Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

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.