Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data

• URL: http://arxiv.org/abs/2505.02130v1
• Date: Sun, 04 May 2025 14:40:31 GMT
• Title: Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
• Authors: Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianpin Fan,
• Abstract summary: We study how large language models (LLMs) process graph-structured data.<n>We uncover unique phenomena regarding how LLMs apply attention to graph-structured data.<n>We analyze these findings to improve the modeling of such data by LLMs.
• Score: 10.907949155931474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attention mechanisms are critical to the success of large language models (LLMs), driving significant advancements in multiple fields. However, for graph-structured data, which requires emphasis on topological connections, they fall short compared to message-passing mechanisms on fixed links, such as those employed by Graph Neural Networks (GNNs). This raises a question: ``Does attention fail for graphs in natural language settings?'' Motivated by these observations, we embarked on an empirical study from the perspective of attention mechanisms to explore how LLMs process graph-structured data. The goal is to gain deeper insights into the attention behavior of LLMs over graph structures. We uncovered unique phenomena regarding how LLMs apply attention to graph-structured data and analyzed these findings to improve the modeling of such data by LLMs. The primary findings of our research are: 1) While LLMs can recognize graph data and capture text-node interactions, they struggle to model inter-node relationships within graph structures due to inherent architectural constraints. 2) The attention distribution of LLMs across graph nodes does not align with ideal structural patterns, indicating a failure to adapt to graph topology nuances. 3) Neither fully connected attention nor fixed connectivity is optimal; each has specific limitations in its application scenarios. Instead, intermediate-state attention windows improve LLM training performance and seamlessly transition to fully connected windows during inference. Source code: \href{https://github.com/millioniron/LLM_exploration}{LLM4Exploration}

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