Abstract
Knowledge graph question answering (KGQA) is a promising approach for mitigating LLM hallucination by grounding reasoning in structured and verifiable knowledge graphs. Existing approaches fall into two paradigms: retrieval-based methods utilize small specialized models, which are efficient but often produce unreachable paths and miss implicit constraints, while agent-based methods utilize large general models, which achieve stronger structural grounding at substantially higher cost. We propose RouterKGQA, a framework for specialized--general model collaboration, in which a specialized model generates reasoning paths and a general model performs KG-guided repair only when needed, improving performance at minimal cost. We further equip the specialized with constraint-aware answer filtering, which reduces redundant answers. In addition, we design a more efficient general agent workflow, further lowering inference cost. Experimental results show that RouterKGQA outperforms the previous best by 3.57 points in F1 and 0.49 points in Hits@1 on average across benchmarks, while requiring only 1.15 average LLM calls per question. Codes and models are available at this https URL.
Abstract (translated)
知识图谱问答（KGQA）是一种通过将推理过程锚定于结构化且可验证的知识图谱来缓解大语言模型（LLM）幻觉问题的前景方法。现有方法分为两类范式：基于检索的方法使用小型专用模型，效率高但常产生不可达路径且遗漏隐式约束；基于智能体的方法使用大型通用模型，虽能实现更强的结构锚定，但成本显著更高。我们提出RouterKGQA框架，实现专用-通用模型协作：由专用模型生成推理路径，仅在必要时由通用模型执行知识图谱引导的修复，从而以最小成本提升性能。我们还为专用模型配备约束感知答案过滤机制，以减少冗余答案。此外，我们设计了更高效的通用智能体工作流，进一步降低推理成本。实验结果表明，RouterKGQA在多个基准测试中平均将F1值提升3.57点、Hits@1提升0.49点，同时每个问题仅需平均1.15次LLM调用。代码与模型已公开于此https链接。
URL
https://arxiv.org/abs/2603.20017
