Graph-RAG systems achieve strong multi-hop question answering by indexing documents into knowledge graphs, but strong retrieval does not guarantee strong answers. Evaluating KET-RAG, a leading Graph-RAG system, on three multi-hop QA benchmarks (HotpotQA, MuSiQue, 2WikiMultiHopQA), we find that 77% to 91% of questions have the gold answer in the retrieved context, yet accuracy is only 35% to 78%, and 73% to 84% of errors are reasoning failures. We propose two augmentations: (i) SPARQL chain-of-thought prompting, which decomposes questions into triple-pattern queries aligned with the entity-relationship context, and (ii) graph-walk compression, which compresses the context by ~60% via knowledge-graph traversal with no LLM calls. SPARQL CoT improves accuracy by +2 to +14 pp; graph-walk compression adds +6 pp on average when paired with structured prompting on smaller models. Surprisingly, we show that, with question-type routing, a fully augmented budget open-weight Llama-8B model matches or exceeds the unaugmented Llama-70B baseline on all three benchmarks at ~12x lower cost. A replication on LightRAG confirms that our augmentations transfer across Graph-RAG systems.

翻译：图检索增强生成系统通过将文档索引为知识图谱来实现强大的多跳问答能力，但高效的检索并不能保证高质量的答案。我们在三个多跳问答基准数据集（HotpotQA、MuSiQue、2WikiMultiHopQA）上评估领先的图检索增强生成系统KET-RAG时发现：77%至91%的问题其标准答案已存在于检索到的上下文中，但系统准确率仅为35%至78%，且73%至84%的错误源于推理失败。我们提出两种增强方案：（一）SPARQL思维链提示，该方法将问题分解为与实体关系上下文对齐的三元组查询模式；（二）图遍历压缩，通过知识图谱遍历将上下文压缩约60%且无需调用大语言模型。SPARQL思维链提示将准确率提升2至14个百分点；当与结构化提示结合应用于较小模型时，图遍历压缩平均可额外提升6个百分点。值得注意的是，通过问题类型路由机制，完全增强的预算开放权重Llama-8B模型在所有三个基准测试中均达到或超过未增强的Llama-70B基线水平，而成本降低约12倍。在LightRAG上的复现实验证实，我们的增强方案可跨图检索增强生成系统迁移。