超越链式结构：在复杂问答中桥接大语言模型与知识库 (Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering)

Knowledge Base Question Answering (KBQA) aims to answer natural language questions using structured knowledge from KBs. While LLM-only approaches offer generalization, they suffer from outdated knowledge, hallucinations, and lack of transparency. Chain-based KG-RAG methods address these issues by incorporating external KBs, but are limited to simple chain-structured questions due to the absence of planning and logical structuring. Inspired by semantic parsing methods, we propose PDRR: a four-stage framework consisting of Predict, Decompose, Retrieve, and Reason. Our method first predicts the question type and decomposes the question into structured triples. Then retrieves relevant information from KBs and guides the LLM as an agent to reason over and complete the decomposed triples. Experimental results demonstrate that PDRR consistently outperforms existing methods across various LLM backbones and achieves superior performance on both chain-structured and non-chain complex questions.

翻译：知识库问答旨在利用知识库中的结构化知识回答自然语言问题。尽管纯大语言模型方法具备泛化能力，但其存在知识过时、幻觉生成和缺乏透明度等问题。基于链式结构的KG-RAG方法通过引入外部知识库解决了这些问题，但由于缺乏规划与逻辑结构化能力，仅适用于简单的链式结构问题。受语义解析方法启发，我们提出PDRR框架：包含预测、分解、检索与推理的四阶段方法。该方法首先预测问题类型并将问题分解为结构化三元组，随后从知识库中检索相关信息，并引导大语言模型作为智能体对分解后的三元组进行推理与补全。实验结果表明，PDRR在不同大语言模型基座上均持续优于现有方法，并在链式与非链式复杂问题上均表现出优越性能。