RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917354844192768 |
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| author | Yuan, Bo Deng, Hexuan Liu, Xuebo Zhang, Min |
| author_facet | Yuan, Bo Deng, Hexuan Liu, Xuebo Zhang, Min |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_20017 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering Yuan, Bo Deng, Hexuan Liu, Xuebo Zhang, Min Computation and Language Databases Information Retrieval 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. |
| title | RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering |
| topic | Computation and Language Databases Information Retrieval |
| url | https://arxiv.org/abs/2603.20017 |