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Although large language models (LLMs) have shown remarkable capability in using textual documents, leveraging structured knowledge remains a significant challenge. To limit the search scope, recent retrieve-then-read methods have to prune the knowledge graph (KG) prematurely, resulting in the destructive loss of long, multi-hop paths. Although existing semantic parsing methods increase the coverage of answers by executing queries on KGs, similar yet irrelevant noise paths ultimately confuse the LLM, making it difficult to distinguish the correct answer. To address these challenges, we propose a novel backward chaining method, BackChainer, that generates knowledge paths for LLMs by working backward from reasoning possible answers over KGs. We introduce a global KG reasoner to locate candidate answers on the global KG and a constrained path generator to backtrack focused knowledge paths for LLMs. Extensive experiments show that BackChainer outperforms existing KGQA baselines and achieves stable improvements on different LLM backbones. BackChainer demonstrates a high answer hit rate and can integrate with various LLMs using only 0.04% of the trainable parameters compared to state-of-the-art methods.

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BackChainer: Backward Chaining over Graph for Integrating Structured Knowledge Into Large Language Model Reasoning

  • 会议论文

  • 发表日期：

    2026年

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  • Ruilin Zhao,
  • Feng Zhao,
  • Guandong Xu

摘要

Although large language models (LLMs) have shown remarkable capability in using textual documents, leveraging structured knowledge remains a significant challenge. To limit the search scope, recent retrieve-then-read methods have to prune the knowledge graph (KG) prematurely, resulting in the destructive loss of long, multi-hop paths. Although existing semantic parsing methods increase the coverage of answers by executing queries on KGs, similar yet irrelevant noise paths ultimately confuse the LLM, making it difficult to distinguish the correct answer. To address these challenges, we propose a novel backward chaining method, BackChainer, that generates knowledge paths for LLMs by working backward from reasoning possible answers over KGs. We introduce a global KG reasoner to locate candidate answers on the global KG and a constrained path generator to backtrack focused knowledge paths for LLMs. Extensive experiments show that BackChainer outperforms existing KGQA baselines and achieves stable improvements on different LLM backbones. BackChainer demonstrates a high answer hit rate and can integrate with various LLMs using only 0.04% of the trainable parameters compared to state-of-the-art methods.