Efficient Graph Representation Learning with Large Language Models through Parameter-Efficient Tuning

Core Concepts

This paper introduces Graph-aware Parameter-Efficient Fine-Tuning (GPEFT), a novel approach for efficient graph representation learning with large language models (LLMs) on text-rich graphs. GPEFT leverages graph neural networks to encode structural information into a graph prompt, which is then integrated into the LLM input sequence. This allows GPEFT to directly generate node embeddings from LLMs with an affordable fine-tuning cost.

Abstract

This paper proposes a novel framework called Graph-aware Parameter-Efficient Fine-Tuning (GPEFT) for efficient graph representation learning using large language models (LLMs) on text-rich graphs. Key highlights: GPEFT utilizes a graph neural network (GNN) to encode the structural information of neighboring nodes into a "graph prompt", which is then prepended to the text sequence input to the LLM. To align the feature space of the GNN-generated prompt with the LLM's text embeddings, GPEFT employs a pre-training phase where the GNN is trained to assist the frozen LLM in predicting the next token in the node text. During fine-tuning, GPEFT optimizes the GNN prompt encoder and a small set of PEFT parameters, while keeping the majority of the LLM frozen. This ensures computational efficiency. Comprehensive experiments on 8 different text-rich graphs demonstrate that GPEFT outperforms existing GNN-LM approaches by 2-3% in link prediction tasks, while being significantly more parameter-efficient. GPEFT can be smoothly integrated with various large language models, including OPT, LLaMA and Falcon.

Stats

GPEFT achieves an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) over existing methods in link prediction evaluations. GPEFT introduces only 14.4M additional trainable parameters per task, which is significantly fewer than existing GNN-LMs (at least 110M).

Quotes

"Contrary to PEFT, another line of research [6, 45] explores in-context learning of LLMs without weight updates. These works transform the neighboring context of a target node into textual descriptions, incorporating features, and employ LLMs to make predictions such as node degrees, labels, and the existence of links." "Compared with existing GNN-LMs, we reduce the training cost of GNN-LMs by parameter-efficient tuning and use structural information as input instead of a nested architecture; Compared with In-Graph Context Learning LLMs, we avoid the quadratic inference costs for link prediction."

Key Insights Distilled From

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning

by Qi Zhu,Da Zh... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18271.pdf

Deeper Inquiries

How can GPEFT be extended to handle heterogeneous text-rich graphs with multiple node and edge types

To extend GPEFT to handle heterogeneous text-rich graphs with multiple node and edge types, we can modify the graph prompt encoder to incorporate information about the different types of nodes and edges. This can be achieved by enhancing the GNN architecture to handle multiple types of inputs and outputs. Specifically, we can introduce separate branches in the GNN for each node and edge type, allowing the model to learn distinct representations for different types of entities in the graph. Additionally, we can incorporate attention mechanisms or learnable embeddings to capture the relationships between different types of nodes and edges in the graph. By adapting the GNN prompt encoder to handle heterogeneous graphs, we can effectively leverage the rich textual and structural information present in such complex networks.

What are the potential limitations of using a GNN prompt encoder, and how could alternative structural encoding methods be explored

While the GNN prompt encoder in GPEFT offers a powerful way to integrate structural information into large language models for graph representation learning, there are potential limitations to consider. One limitation is the scalability of the GNN prompt encoder, as processing large and complex graphs with a high number of nodes and edges may lead to increased computational complexity and memory requirements. Additionally, the GNN prompt encoder may struggle to capture intricate relationships and dependencies in the graph, especially in cases where the graph structure is highly dynamic or sparse. To address these limitations, alternative structural encoding methods can be explored. One approach is to combine the GNN prompt encoder with graph attention networks (GATs) to capture more nuanced relationships between nodes and edges. GATs can adaptively assign different weights to neighboring nodes based on their importance, allowing for more flexible and expressive graph representations. Another approach is to incorporate graph convolutional networks (GCNs) to capture higher-order dependencies in the graph structure. By experimenting with different structural encoding methods and architectures, we can enhance the robustness and scalability of GPEFT for handling diverse text-rich graphs.

Given the success of GPEFT in graph representation learning, how could the ideas be applied to other domains that leverage both textual and structured data, such as knowledge graphs or recommender systems

The success of GPEFT in graph representation learning can be applied to other domains that leverage both textual and structured data, such as knowledge graphs or recommender systems, by adapting the framework to suit the specific characteristics of these domains. For knowledge graphs, GPEFT can be extended to incorporate domain-specific knowledge graphs and ontologies, enabling the model to learn rich representations that capture both textual information and semantic relationships between entities. By pre-training the GNN prompt encoder on knowledge graph data and fine-tuning it with large language models, GPEFT can effectively learn representations that encode both textual and structural information in the knowledge graph. In the context of recommender systems, GPEFT can be tailored to handle user-item interaction data, incorporating user preferences, item attributes, and contextual information. By integrating GNNs with large language models in the recommendation pipeline, GPEFT can generate personalized recommendations based on a combination of textual features and graph structures. Additionally, by fine-tuning the model on user-item interaction data, GPEFT can adapt to user preferences and provide more accurate and relevant recommendations. Overall, the ideas and methodologies of GPEFT can be applied to a wide range of domains that require the integration of textual and structured data for enhanced modeling and prediction tasks.