用分层结构提升大图任务中LLM的理解与解释能力
A Hierarchical Language Model For Interpretable Graph Reasoning
- 分两块设计：聚焦节点局部信息与边的全局结构
- 在多类图任务中表现更优，计算开销更低
- 通过注意力权重实现模型可解释性，适合研究者参考
大型语言模型（LLMs）正被广泛探索用于图任务。尽管在文本任务中表现卓越，但其对显式图结构的理解能力仍有限，尤其在大规模图上。本文提出分层图语言模型（HLM-G），采用双模块架构，分别捕捉节点中心的局部信息与交互中心的全局结构，显著提升图结构理解能力。该方法使LLM能高效、稳健地处理各类图查询，同时降低大规模图任务的计算成本。我们通过内在注意力权重和现有解释工具验证了模型的可解释性。在节点、链接和图级别多种图推理及真实世界任务上的全面评估表明，该方法性能优越，标志着LLM应用于图理解的重要进展。
原文摘要 · Abstract (English)
Large language models (LLMs) are being increasingly explored for graph tasks. Despite their remarkable success in text-based tasks, LLMs' capabilities in understanding explicit graph structures remain limited, particularly with large graphs. In this work, we introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to capture node-centric local information and interaction-centric global structure, effectively enhancing graph structure understanding abilities. The proposed scheme allows LLMs to address various graph queries with high efficacy, efficiency, and robustness, while reducing computational costs on large-scale graph tasks. Furthermore, we demonstrate the interpretability of our model using intrinsic attention weights and established explainers. Comprehensive evaluations across diverse graph reasoning and real-world tasks of node, link, and graph-levels highlight the superiority of our method, marking a significant advancement in the application of LLMs to graph understanding.
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