HuixiangDou2: A Robustly Optimized GraphRAG Approach

• URL: http://arxiv.org/abs/2503.06474v1
• Date: Sun, 09 Mar 2025 06:20:24 GMT
• Title: HuixiangDou2: A Robustly Optimized GraphRAG Approach
• Authors: Huanjun Kong, Zhefan Wang, Chenyang Wang, Zhe Ma, Nanqing Dong,
• Abstract summary: Graph-based Retrieval-Augmented Generation (GraphRAG) addresses this by structuring it as a graph for dynamic retrieval.<n>We introduce HuixiangDou2, a robustly optimized GraphRAG framework.<n>Specifically, we leverage the effectiveness of dual-level retrieval and optimize its performance in a 32k context.
• Score: 11.91228019623924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) perform well on familiar queries but struggle with specialized or emerging topics. Graph-based Retrieval-Augmented Generation (GraphRAG) addresses this by structuring domain knowledge as a graph for dynamic retrieval. However, existing pipelines involve complex engineering workflows, making it difficult to isolate the impact of individual components. Evaluating retrieval effectiveness is also challenging due to dataset overlap with LLM pretraining data. In this work, we introduce HuixiangDou2, a robustly optimized GraphRAG framework. Specifically, we leverage the effectiveness of dual-level retrieval and optimize its performance in a 32k context for maximum precision, and compare logic-based retrieval and dual-level retrieval to enhance overall functionality. Our implementation includes comparative experiments on a test set, where Qwen2.5-7B-Instruct initially underperformed. With our approach, the score improved significantly from 60 to 74.5, as illustrated in the Figure. Experiments on domain-specific datasets reveal that dual-level retrieval enhances fuzzy matching, while logic-form retrieval improves structured reasoning. Furthermore, we propose a multi-stage verification mechanism to improve retrieval robustness without increasing computational cost. Empirical results show significant accuracy gains over baselines, highlighting the importance of adaptive retrieval. To support research and adoption, we release HuixiangDou2 as an open-source resource https://github.com/tpoisonooo/huixiangdou2.

Related papers

• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task. Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively. We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance. We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [84.41557981816077]
  We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.<n>GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.<n>It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
  arXiv  Detail & Related papers  (2025-02-03T07:04:29Z)
• TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG [3.8704987495086542]
  TOBUGraph is a graph-based retrieval framework that first constructs the knowledge graph from unstructured data. It extracts structured knowledge and diverse relationships among data, going beyond RAG's text-to-text similarity. We demonstrate TOBUGraph's effectiveness in TOBU, a real-world application in production for personal memory organization and retrieval.
  arXiv  Detail & Related papers  (2024-12-06T22:05:39Z)
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs. Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data [51.47827479376251]
  LOCAL is a highly efficient, easy-to-implement, and constraint-free method for recovering dynamic causal structures. Asymptotic Causal Learning Mask (ACML) and Dynamic Graph Learning (DGPL) Experiments on synthetic and real-world datasets demonstrate that LOCAL significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2024-10-25T10:48:41Z)
• Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing [42.6340751096123]
  We investigate the problem of hashing with Graph Convolutional Network for effective Top-N search. Our findings indicate the learning effectiveness of incorporating hashing techniques within the exploration of bipartite graph reception fields. We propose Bipartite Graph Contrastive Hashing (BGCH+) to enhance the model performance.
  arXiv  Detail & Related papers  (2024-08-17T16:21:32Z)
• Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [14.448198170932226]
  Think-on-Graph 2.0 (ToG-2) is a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources.<n>ToG-2 alternates between graph retrieval and context retrieval to search for in-depth clues relevant to the question.<n>It achieves overall state-of-the-art (SOTA) performance on 6 out of 7 knowledge-intensive datasets with GPT-3.5.
  arXiv  Detail & Related papers  (2024-07-15T15:20:40Z)
• Amplify Graph Learning for Recommendation via Sparsity Completion [16.32861024767423]
  Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges. We propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC)
  arXiv  Detail & Related papers  (2024-06-27T08:26:20Z)
• Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient [52.2669490431145]
  PropEn is inspired by'matching', which enables implicit guidance without training a discriminator. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution.
  arXiv  Detail & Related papers  (2024-05-28T11:30:19Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Comparative Code Structure Analysis using Deep Learning for Performance Prediction [18.226950022938954]
  This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. Our evaluations of several deep embedding learning methods demonstrate that tree-based Long Short-Term Memory (LSTM) models can leverage the hierarchical structure of source-code to discover latent representations and achieve up to 84% (individual problem) and 73% (combined dataset with multiple of problems) accuracy in predicting the change in performance.
  arXiv  Detail & Related papers  (2021-02-12T16:59:12Z)
• Deep Reinforcement Learning of Graph Matching [63.469961545293756]
  Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
  arXiv  Detail & Related papers  (2020-12-16T13:48:48Z)

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.