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Large language models (LLMs) have demonstrated significant potential in complex question answering (QA) by reasoning on knowledge graphs (KGs), which offer structured external knowledge. Existing methods for KG-augmented reasoning, including iterative and once paradigms, have enhanced LLMs’ performance in complex QA and emphasized the significance of reasoning through KG relations. However, a significant challenge remains in constructing optimal reasoning plans from KG relations while maximizing their reasoning capabilities. To address this challenge, we propose Graph-of-Thought Guided Reasoning (GoT-R), which enhances LLMs for complex QA. GoT-R integrates three components: (i) atomic relations selection, which uses an encoder to identify relevant relations; (ii) graph-of-thought (GoT) construction, which builds a graph-of-thought combining KG-aligned relations and LLM’s inherent knowledge; and (iii) GoT-guided hybrid retrieval and reasoning, which integrates relation-aware and relation-unaware retrieval to generate reliable and comprehensive reasoning evidence. By leveraging KG relations and LLM knowledge, GoT-R achieves faithful and interpretable reasoning while reducing reliance on high-quality QA datasets. Experimental results on four complex QA datasets demonstrate GoT-R’s effectiveness and generalizability. The source code for this project is available at https://github.com/Peixuan-Huang/GoT-R .

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GoT-R: Enhancing Large Language Models for Complex Question Answering with Graph-of-Thought Guided Reasoning

  • 会议论文

  • 发表日期：

    2026年

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  • Peixuan Huang,
  • Bohan Li,
  • Wenlong Wu,
  • Haofen Wang,
  • Mengfei Xu,
  • Chen Chen,
  • Lei Liang,
  • Meng Wang

摘要

Large language models (LLMs) have demonstrated significant potential in complex question answering (QA) by reasoning on knowledge graphs (KGs), which offer structured external knowledge. Existing methods for KG-augmented reasoning, including iterative and once paradigms, have enhanced LLMs’ performance in complex QA and emphasized the significance of reasoning through KG relations. However, a significant challenge remains in constructing optimal reasoning plans from KG relations while maximizing their reasoning capabilities. To address this challenge, we propose Graph-of-Thought Guided Reasoning (GoT-R), which enhances LLMs for complex QA. GoT-R integrates three components: (i) atomic relations selection, which uses an encoder to identify relevant relations; (ii) graph-of-thought (GoT) construction, which builds a graph-of-thought combining KG-aligned relations and LLM’s inherent knowledge; and (iii) GoT-guided hybrid retrieval and reasoning, which integrates relation-aware and relation-unaware retrieval to generate reliable and comprehensive reasoning evidence. By leveraging KG relations and LLM knowledge, GoT-R achieves faithful and interpretable reasoning while reducing reliance on high-quality QA datasets. Experimental results on four complex QA datasets demonstrate GoT-R’s effectiveness and generalizability. The source code for this project is available at https://github.com/Peixuan-Huang/GoT-R .