NGM-RAG is introduced, a novel framework that leverages graph structures to effectively capture and utilize relational knowledge for improved retrieval and answer generation and proposes a neural graph matching approach that combines text-based matching with Graph Neural Networks (GNNs).
Abstract
Retrieval-Augmented Generation (RAG) significantly enhances the ability of Large Language Models (LLMs) to provide accurate and contextually relevant answers by dynamically integrating external databases. However, traditional RAG methods are primarily constrained by their reliance on text-based retrieval strategies, which often struggle with complex questions requiring multi-hop reasoning. To address this limitation, we introduce Neural Graph Matching based Retrieval-Augmented Generation (NGM-RAG), a novel framework that leverages graph structures to effectively capture and utilize relational knowledge for improved retrieval and answer generation. NGM-RAG explicitly incorporates graph construction, graph matching, and answer generation into a unified process. Within this framework, we propose a neural graph matching approach that combines text-based matching with Graph Neural Networks (GNNs). By employing an adaptive weighting strategy, NGM-RAG efficiently integrates multiple matching methods to select the most relevant contextual node information for answer generation. Experimental results on multi-hop question answering and long-context summarization tasks demonstrate that our NGM-RAG model achieves superior performance compared to both traditional NaiveRAG methods and state-of-the-art graph-enhanced approaches such as GraphRAG and LightRAG.
SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever, suggests that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
MeAI++ is proposed, a novel framework that integrates knowledge graph based retrieval with a reinforcement learning (RL) optimization loop to jointly enhance retrieval and generation and confirms the effectiveness and generalizability of MeAI++ for complex, knowledge-intensive question answering.
Tram Nguyen, Truong H. V. Phan· Journal of Intelligent &...· 0 citations
This work introduces a GLM-based retriever and investigates the comparative strengths of GLM-based, GNN-based, and traditional vector-search-based retrievers in single- and multi-hop RAG settings, and suggests that finetuned GLM retrievers generalize better out of domain.
Retrieval-Augmented Generation (RAG) has become a fundamental paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, while recent structure-augmented approaches organize documents into graphs to improve information access, their retrieval strategies remain largely static, relying on similarity ranking or static probability diffusion. We identify that this paradigm suffers from two inherent limitations in complex reasoning: popularity bias, where retrieval paths are trapped by high-degree distractors, and signal decay, where relevance signals attenuate over long reasoning chains. To overcome these challenges, we propose NaviRAG, a novel framework that reformulates retrieval as a reinforcement learning-driven dynamic navigation problem on schema-less knowledge graphs (KGs). Unlike passive diffusion, NaviRAG employs an agent that actively traverses the graph to act as a search-space pruning engine, identifying logical multi-hop reasoning paths. Technically, we introduce three key components: (1) Structure-Aware Query Expansion, which bridges the modality gap between unstructured queries and structured graph seeds for precise initialization; (2) Target-Driven Reward Shaping, which provides dense supervision based on semantic progress toward gold documents, effectively mitigating the sparse reward problem in large-scale graph traversal; and (3) a Multi-View Hybrid Reranking strategy that operates on the highly-pruned candidate subgraph, integrating policy confidence, semantic relevance, and global structural importance to ensure robust candidate selection. Extensive experiments on three multi-hop QA datasets and two single-hop QA datasets demonstrate that NaviRAG significantly outperforms baselines, achieving state-of-the-art performance in multi-hop QA while maintaining robustness in single-hop QA. Our code and data are available at https://github.com/CkingEW/NaviRAG.
Jinghong Lei, Wang Kun, Zhigang Chen et al.· Proceedings of the 32nd ACM...· 0 citations
This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between queries and candidate passages, significantly enhancing retrieval ranking precision and semantic robustness.
Jiali Jiang, Chih-Yung Chang, Youxi Li et al.· Multimedia Systems· 0 citations
Retrieval-Augmented Generation (RAG) is widely used to enhance question-answering systems across various domains. However, while real-world source documents are inherently structured, conventional RAG approaches primarily rely on semantic similarity between isolated text chunks, which can overlook document hierarchy and limit retrieval effectiveness. To address this issue, this study introduces a document hierarchy-based Chunk Graph approach to improve retrieval grounding in RAG systems. The proposed framework preserves document hierarchy during chunking and models inter-chunk relationships using a weighted graph that combines structural proximity and semantic similarity. The approach was evaluated using the StructuredQA and CUAD benchmark datasets, with performance measured via Precision, Recall, and F1-Score. Experimental results demonstrate that the effectiveness of the Chunk Graph depends heavily on the source document format. On the highly structured StructuredQA dataset, the proposed method successfully connects fragmented information, improving the F1-Score from 47.62% to 50.23%. Conversely, on the CUAD dataset which consists of raw text with implicit hierarchy and no nested structure, the model becomes redundant and does not yield performance gains. These findings conclude that integrating structural-semantic relationships significantly improves context selection, specifically for documents with explicit hierarchical structures.
P. Cristin, Hilmil Pradana· JOURNAL OF APPLIED INFORMATI...· 0 citations
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