发表机构
Xinjiang University（新疆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对图增强LLM检验图证据可被原生解码器使用的假设，通过HopQA诊断任务发现现有模型无法有效利用图证据，提出S²GE方法在多个数据集上大幅提升性能，并揭示了相关 regimes。
AI 中文摘要
图增强大型语言模型通常假设，外部计算生成并置于输入中的图证据可被原生解码器使用。我们通过HopQA（一项特意设置边界的诊断任务，要求查询两个节点间的最短跳数距离）检验该假设。由于答案是小整数且目标完全是拓扑性的，失败不能被归咎于开放式生成或模糊评估。然而，现有图增强基线在该设置下仍失败，表明提供图证据与使其可用并非同一回事。我们引入干预三角，包含三个匹配条件：可读图证据、打乱的图证据和无图输入，以此区分证据包含、结构可读性和解码器可用拓扑。基于此诊断，我们提出S²GE作为实例，表明诊断驱动的接口设计可提升原生解码器可用性。S²GE采用查询感知采样、基于端点和邻近度的排序，以及保结构对齐。在DBLP、Biomedical、GoodReads和PubMed数据集上，S²GE分别取得36.5%、57.8%、76.6%和52.0%的严格精确匹配分数，平均比最强的原生生成基线提升53.5个百分点。这些干预进一步揭示了有害打乱、打乱鲁棒和无图饱和等 regimes。
英文摘要
Graph-augmented large language models often assume that graph evidence produced by external computation and placed in the input can be used by the native decoder. We test this assumption with HopQA, a deliberately bounded diagnostic that asks for the shortest-hop distance between two query nodes. Because the answer is a small integer and the target is purely topological, failure cannot be dismissed as open-ended generation or ambiguous evaluation. Yet existing graph-augmented baselines still fail on this setting, showing that providing graph evidence is not the same as making it usable. We introduce an intervention triangle with three matched conditions: readable graph evidence, shuffled graph evidence, and no-graph input. This separates evidence inclusion, structural readability, and decoder-usable topology. Guided by this diagnosis, we present S$^2$GE as an instance showing that diagnosis-driven interface design can improve native decoder usability. S$^2$GE uses query-aware sampling, endpoint and proximity-based ordering, and structure-preserving alignment. Across DBLP, Biomedical, GoodReads, and PubMed, S$^2$GE achieves strict exact-match scores of $36.5\%$, $57.8\%$, $76.6\%$, and $52.0\%$, improving over the strongest native-generation baseline by $53.5$ points on average. The interventions further reveal harmful-shuffle, shuffle-robust, and no-graph-saturated regimes.
Comments18 pages, 4 figures, accepted at EMNLP 2026 (Main Conference)
