RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering

• URL: http://arxiv.org/abs/2603.20017v1
• Date: Fri, 20 Mar 2026 15:01:57 GMT
• Title: RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering
• Abstract summary: KGQA is a promising approach for mitigating LLM hallucination by grounding reasoning in structured and verifiable knowledge graphs.<n>We propose RouterKGQA, a framework for specialized--general model collaboration.<n>We show that RouterKGQA outperforms the previous best by 3.57 points in F1 and 0.49 points in Hits@1 on average across benchmarks.
• Score: 23.627999197728403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/Oldcircle/RouterKGQA.

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