让大模型更懂复杂问题，用知识库精准回答。
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
- 分四步预测、拆解、检索、推理，系统化处理复杂问题
- 在多种大模型上表现更优，尤其擅长非链式复杂问答
- 适合需要准确推理和透明过程的智能问答场景
知识库问答（KBQA）旨在利用结构化知识库回答自然语言问题。尽管纯大模型方法具备泛化能力，但存在知识过时、幻觉频发和可解释性差的问题。基于链条的KG-RAG方法虽通过引入外部知识库缓解这些问题，却受限于仅能处理链式结构问题，缺乏规划与逻辑组织能力。受语义解析启发，我们提出PDRR：一个包含预测、分解、检索和推理四个阶段的框架。该方法首先预测问题类型并将其分解为结构化三元组，随后从知识库中检索相关信息，并引导大模型作为代理对分解后的三元组进行推理与补全。实验表明，PDRR在不同大模型底座下均持续优于现有方法，在链式与非链式复杂问题上均取得更优性能。
原文摘要 · Abstract (English)
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 While LLM-only approaches offer generalization, they suffer from outdated knowledge, hallucinations, and lack of transparency.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.
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