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

Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianpin Fan

TL;DR

The paper investigates whether attention mechanisms in large language models can effectively process graph-structured data. Through an attention-centric empirical study, it analyzes attention distributions, disruption effects on topology, and the impact of varying attention windows across multiple graph datasets. It uncovers phenomena such as Attention Sink and Skewed Line Sink, showing that LLMs struggle to leverage inter-node connectivity and that fully connected attention windows are not optimal for graphs. It proposes intermediate, topology-aware attention perspectives that improve training and transferability to larger horizons, offering practical guidance for designing graph-aware LLMs and guiding future research.

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}

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

TL;DR

The paper investigates whether attention mechanisms in large language models can effectively process graph-structured data. Through an attention-centric empirical study, it analyzes attention distributions, disruption effects on topology, and the impact of varying attention windows across multiple graph datasets. It uncovers phenomena such as Attention Sink and Skewed Line Sink, showing that LLMs struggle to leverage inter-node connectivity and that fully connected attention windows are not optimal for graphs. It proposes intermediate, topology-aware attention perspectives that improve training and transferability to larger horizons, offering practical guidance for designing graph-aware LLMs and guiding future research.

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}
Paper Structure (21 sections, 18 figures, 15 tables)

This paper contains 21 sections, 18 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Analysis of Attention in LLMs
  4. Attention Window
  5. Empirical Study
  6. Findings and Conclusion
  7. Acknowledgements
  8. Implementation Details
  9. Datasets
  10. Additional Related Work
  11. Other Base Models for Disruption Performance
  12. Statistical Analysis of Attention Score Distributions
  13. Disruption Attention Score
  14. Attention Score Matrix(Tokens)
  15. Token to Token
  16. ...and 6 more sections

Figures (18)

  • Figure 1: Attention distribution of different types of tokens before and after training. With Amazon-Ratings on the left, Roman-Empire in the middle, and Wikics on the right. The attention values have undergone log scaling and are plotted as a density distribution Figure.
  • Figure 2: The attention scores from neighboring nodes to the central node, both before and after training, were presented as mean values with standard deviations, using a 1:8 sampling ratio.
  • Figure 3: Illustration of all tokens to nodes attention. The x-coordinate refers to the relative position of the node token in the entire node list. We collected the attention scores of all tokens towards node tokens and plotted them in a line graph according to the relative position of nodes within the instructions. Upper: the mean values of attention scores from nodes(Querys) to nodes(Keys). Lower: the mean values of attention scores from texts(Querys) to nodes(Keys). The plot annotates nodes at different hierarchical levels. Our node sampling is 8*8.
  • Figure 4: Padding and Random Shuffling to ensure fixed sequence length and position. This minimizes interference with attention scores for other reasons.
  • Figure 5: Illustration of attention score interaction matrix(Nodes). Left: The left panel displays the attention interaction matrix between all nodes (1 central node + 8 first-order nodes + 8*8 second-order nodes), averaged across all heads and layers. Right: The right panel shows the attention interaction matrices between first-order and second-order nodes, with four selected groups highlighted for detailed analysis. We highlight the "attention sink" with a gray box and the "Skewed Line Sink" with a black box.
  • ...and 13 more figures