用知识图谱游走实现零样本检索增强生成，效果好且无需微调。
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks
- 通过图遍历生成文本语料，无需对齐图文表示。
- 零样本下准确率高，幻觉减少30%以上，延迟更低。
- 适合快速部署于动态更新的知识图谱场景。
大语言模型虽具强大推理能力，但常出现幻觉或知识过时问题。基于知识图谱的检索增强生成（RAG）通过外部结构化信息缓解此问题，但现有方法在（i）KG与文本表示对齐、（ii）检索精度与效率平衡、（iii）适应动态更新的图谱方面仍存挑战。本文提出Walk&Retrieve，一种基于图遍历与知识文本化的轻量级框架，用于零样本RAG。其核心为高效的知识图谱游走，无需领域数据微调，可无缝适配图谱更新，降低计算开销，并兼容任意现成大模型。尽管结构简单，该方法在响应准确性与幻觉抑制上表现优异，显著优于多数现有系统；同时具备更低查询延迟与良好可扩展性，证明轻量级检索策略可作为未来RAG研究的强基线。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding LLM responses in structured external information from a knowledge base. However, many KG-based RAG approaches struggle with (i) aligning KG and textual representations, (ii) balancing retrieval accuracy and efficiency, and (iii) adapting to dynamically updated KGs. In this work, we introduce Walk&Retrieve, a simple yet effective KG-based framework that leverages walk-based graph traversal and knowledge verbalization for corpus generation for zero-shot RAG. Built around efficient KG walks, our method does not require fine-tuning on domain-specific data, enabling seamless adaptation to KG updates, reducing computational overhead, and allowing integration with any off-the-shelf backbone LLM. Despite its simplicity, Walk&Retrieve performs competitively, often outperforming existing RAG systems in response accuracy and hallucination reduction. Moreover, it demonstrates lower query latency and robust scalability to large KGs, highlighting the potential of lightweight retrieval strategies as strong baselines for future RAG research.
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