通过融合查询增强与图向量检索，提升问答系统准确性。
Augmenting Question Answering with A Hybrid RAG Approach
- 结合查询增强与图-向量双模检索，优化信息召回。
- 在五个大模型上，三数据集测试中准确率均显著提升。
- 适合需要高精度问答的智能客服、知识库应用。
检索增强生成（RAG）已成为提升问答任务响应质量的强大技术。然而，现有方法常因无法有效检索上下文相关信息，导致回答不完整或效果不佳。本文提出结构化语义RAG（SSRAG），一种混合架构，通过引入查询增强、代理路由机制，以及结合向量与图技术的结构化检索方式，并实现上下文统一。该方法通过优化检索流程与增强上下文定位能力，提升了回答的准确性和信息量。我们在三个主流QA数据集（TruthfulQA、SQuAD、WikiQA）上，针对五种大语言模型进行了广泛评估，结果表明，所提方法在各项指标上均持续优于标准RAG实现。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the quality of responses in Question-Answering (QA) tasks. However, existing approaches often struggle with retrieving contextually relevant information, leading to incomplete or suboptimal answers. In this paper, we introduce Structured-Semantic RAG (SSRAG), a hybrid architecture that enhances QA quality by integrating query augmentation, agentic routing, and a structured retrieval mechanism combining vector and graph based techniques with context unification. By refining retrieval processes and improving contextual grounding, our approach improves both answer accuracy and informativeness. We conduct extensive evaluations on three popular QA datasets, TruthfulQA, SQuAD and WikiQA, across five Large Language Models (LLMs), demonstrating that our proposed approach consistently improves response quality over standard RAG implementations.
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