用答案标签自动生成路径监督，让大模型更准答知识图谱问题。
PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering
- 通过轻量Transformer估算关系路径信息量，生成伪路径监督信号。
- 在三个基准上达到顶尖性能，且可提升现有模型效果。
- 无需昂贵人工标注或大模型精修，适合资源有限的研究者。
知识图谱问答（KGQA）旨在通过推理知识图谱（KGs）回答用户问题。现有方法多采用检索增强生成范式，用结构化知识引导大语言模型（LLMs）。但训练有效检索模块需高质量中间监督信号，如相关路径或子图，获取成本高。本文提出PathISE框架，从答案级标签中学习高质量中间监督。PathISE引入轻量级Transformer估计器，评估关系路径的信息量，构建伪路径级监督；该信号被蒸馏至LLM路径生成器，生成的路径在KG中对齐，为归纳式答案推理提供紧凑证据。在三个KGQA基准上的实验表明，PathISE性能媲美或超越当前最优，且可复用监督信号以增强已有模型，无需依赖昂贵的LLM精修信号。代码已公开。
原文摘要 · Abstract (English)
Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the retrieval-augmented generation paradigm to ground Large Language Models~(LLMs) with structured knowledge from KGs. However, training effective models to retrieve question-relevant evidence from KGs typically requires high-quality intermediate supervision signals, such as question-relevant paths or subgraphs, which are time- and resource-intensive to obtain. We propose PathISE, a novel framework for learning high-quality intermediate supervision from answer-level labels. PathISE introduces a lightweight transformer-based estimator that estimates the informativeness of relation paths to construct pseudo path-level supervision. This supervision is then distilled into an LLM path generator, whose generated paths are grounded in the KG to provide compact evidence for inductive answer reasoning. ExtensiveISE experiments on three KGQA benchmarks show that PathISE achieves competitive or state-of-the-art KGQA performance, and provides reusable supervision signals that can enhance existing KGQA models, without relying on costly LLM-refined supervision signals. Our source code is available at https://anonymous.4open.science/r/PathISE-2F87.
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