title: Attention Mechanisms Perspective: Exploring LLM Processing of
    Graph-Structured Data
summary: 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: https://github.com/millioniron/LLM_exploration{LLM4Exploration}
paper_authors._id: 6819f9c7b4ee941e883b2fd2
paper_authors.name: Zhong Guan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-06T16:51:31.791Z
paper_authors._id: 660162a9eee53450ba93c34b
paper_authors.avatarUrl: /avatars/d6128f630e041e29d1cdc178e112f23f.svg
paper_authors.fullname: guanzhong
paper_authors.type: user
paper_authors.user: guanzhong2
paper_authors._id: 6819f9c7b4ee941e883b2fd3
paper_authors.name: Likang Wu
paper_authors._id: 6819f9c7b4ee941e883b2fd4
paper_authors.name: Hongke Zhao
paper_authors._id: 6819f9c7b4ee941e883b2fd5
paper_authors.name: Ming He
paper_authors._id: 6819f9c7b4ee941e883b2fd6
paper_authors.name: Jianpin Fan
paper_title: Attention Mechanisms Perspective: Exploring LLM Processing of
    Graph-Structured Data
paper_summary: 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: https://github.com/millioniron/LLM_exploration{LLM4Exploration}
paper_ai_summary: The study explores how large language models handle graph-structured data, finding that attention mechanisms struggle with inter-node relationships and imply that intermediate-state attention windows can enhance performance.
paper_ai_keywords: attention mechanisms large language models graph-structured data message-passing mechanisms Graph Neural Networks GNNs inter-node relationships attention distribution intermediate-state attention windows