arXiv cs.CL· Sakshi Garg, William B. Andreopoulos·· 11 小时前AI 评分31
用语义分块与混合检索策略改进长文档上下文检索
Improving Contextual Retrieval for Long Documents using Semantic Chunking and Hybrid Retrieval Strategies
AI 导读
研究者提出一种混合 RAG 框架，将知识图谱的结构化推理与稠密向量检索的语义覆盖结合，通过基于 Transformer 的摘要与语义分块的多阶段流水线提升上下文保留。系统构建动态知识图谱捕捉实体间显式关系，推理时优先图遍历、必要时回退向量检索。在 MultiHop-RAG 数据集上，该方法的 Exact Match 分数与检索准确率均优于纯向量和纯图基线。
正文 · 原文
Abstract:The vast amount of information available in lengthy, distributed documents makes finding relevant answers difficult, especially when the answer requires connecting evidence from multiple sources. Standard Retrieval-Augmented Generation (RAG) systems often struggle with this type of "multi-hop" reasoning and frequently face issues with context fragmentation. In this paper, we propose a hybrid RAG framework that integrates the structured reasoning of knowledge graphs with the broad semantic coverage of dense vector retrieval. Our approach employs a multi-stage pipeline using transformer-based summarization and semantic chunking to improve context retention. We construct a dynamic knowledge graph to capture explicit relationships between entities while simultaneously maintaining a dense vector index. During inference, the system uses a hierarchical mechanism that prioritizes graph traversal for precision and falls back to vector retrieval when necessary. We evaluated our framework on the MultiHop-RAG dataset. The results show that this hybrid approach outperforms standalone vector and graph-based baselines, achieving higher Exact Match scores and improved retrieval accuracy for complex queries. This confirms that merging structured reasoning with standard vector retrieval creates a more robust system for handling complex knowledge-intensive questions.
| Subjects: | Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.08498 [cs.IR] |
| (or arXiv:2611.08498v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08498 arXiv-issued DOI via DataCite |
Submission history
From: William Andreopoulos [view email]
[v1]
Wed, 19 Aug 2026 13:48:43 UTC (1,172 KB)
来源：arXiv cs.CL · arxiv.org