用可微分推理打通知识图谱问答的语义鸿沟，提升效率与准确率。
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
- 通过递归软流模块动态更新查询，用结构线索穿越语义不匹配的中间节点。
- 在WebQSP和CWQ上达到领先性能，推理速度远超依赖大模型的复杂方法。
- 适合需要高效精准多跳问答的场景，如智能客服、知识服务系统。
面向知识图谱的多跳问答面临核心挑战：传统检索-阅读流程不可微，导致检索器无法学习弥合查询与中间节点间缺乏词汇重叠的语义鸿沟。为此，我们提出RSF-GLLM框架，将可微图推理与答案生成解耦。其递归软流（RSF）模块采用GRU引导的查询更新机制，传播连续相关性分数，并通过动态门控机制利用结构线索穿越语义差异大的桥接节点。引入流稀疏正则化，理论保证从软概率向离散推理路径收敛。提取的路径被文本化以微调大语言模型（LLM），确保生成结果基于事实拓扑。在WebQSP和CWQ数据集上的实验表明，RSF-GLLM实现竞争性性能，且推理效率显著优于依赖大模型的计算密集型方法。
原文摘要 · Abstract (English)
Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query. To address this, we propose RSF-GLLM, a framework decoupling differentiable graph reasoning from answer generation. Our Recurrent Soft-Flow (RSF) module employs a GRU-guided query updater to propagate continuous relevance scores, utilizing a dynamic gating mechanism to traverse semantically dissimilar bridge nodes via structural cues. We introduce flow sparsity regularization to theoretically guarantee convergence from soft probabilities to discrete reasoning paths. These paths are extracted and textualized to fine-tune a Large Language Model (LLM), ensuring generation is grounded in factual topology. Experiments on WebQSP and CWQ demonstrate that RSF-GLLM achieves competitive performance with superior inference efficiency compared to LLM based computationally expensive approaches.
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