图上前瞻：超越局部视野的知识库问答推理
Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
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中文总结 AI 辅助
针对LLM在知识密集型问答中的幻觉问题，提出图上前瞻（FoG）框架，通过前瞻感知的迭代证据子图构建与远-近反馈引导路径探索，克服局部剪枝的短视，在KBQA基准上达到最先进性能，并在CWQ上实现16.58%的Hit提升。
中文摘要 AI 辅助
大型语言模型（LLMs）在问答任务中展现了强大的能力，但在知识密集型任务上仍频繁出现幻觉。知识图谱（KGs）为LLMs提供了结构化、可解释且可更新的事实依据，使其成为可靠推理的有前景的外部知识来源。然而，现有的LLM引导的图推理方法在证据检索时通常依赖于逐跳的贪婪或束式剪枝。这种局部决策过程本质上是短视的：在源附近看似薄弱的证据，可能只有在探索了更深的图上下文后才变得至关重要，导致关键答案分支被过早丢弃，使推理链难以恢复。为解决这一局限，我们提出了图上前瞻（FoG），一种用于知识库问答（KBQA）的前瞻感知证据检索框架。FoG迭代地构建与问题相关的证据子图，并利用由远及近的反馈引导路径探索，同时维护一个紧凑的记忆子图以支持持续探索。在广泛使用的KBQA基准上的大量实验表明，FoG达到了最先进的性能，在CWQ上的Hit指标取得了16.58%的大幅提升，同时减少了LLM调用次数和令牌使用量。我们的代码可在该https URL获取。
英文摘要
Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge source for reliable reasoning. However, existing LLM-guided graph reasoning methods typically rely on hop-wise greedy or beam-style pruning during evidence retrieval. Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA). FoG iteratively constructs a question-relevant evidence subgraph and uses far-to-near feedback to guide path exploration, and maintains a compact memory subgraph to support continued exploration. Extensive experiments on widely used KBQA benchmarks demonstrate that FoG achieves state-of-the-art performance, with a particularly large improvement of 16.58% in Hit on CWQ, while also reducing LLM calls and token usage. Our code is available at https://github.com/yhong7/FoG .
发表机构
- Tianjin University（天津大学）
- Beijing Jiaotong University（北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。
