Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration

초록

Small language models (SLMs) are increasingly utilized for on-device applications due to their ability to ensure user privacy, reduce inference latency, and operate independently of cloud infrastructure. However, their performance is often limited when processing complex data structures such as graphs, which are ubiquitous in real-world datasets like social networks and system interactions. Graphs inherently encode intricate structural dependencies, requiring models to effectively capture both local and global relationships. Traditional language models, designed primarily for text data, struggle to address these requirements, leading to suboptimal performance in graph-related tasks. To overcome this limitation, we propose a novel graph encoder-based prompt tuning framework which integrates a graph convolutional network (GCN) with a graph transformer. By leveraging the complementary strengths of the GCN for local structural modeling and the graph transformer for capturing global relationships, our method enables SLMs to effectively process graph data. This integration significantly enhances the ability of SLMs to handle graph-centric tasks while maintaining the efficiency required for resource-constrained devices. The experimental results show that our approach not only improves the performance of SLMs on various graph benchmarks but also achieves results which closely approach the performance of a large language model (LLM). This work highlights the potential of extending SLMs for graph-based applications and advancing the capabilities of on-device artificial intelligence.

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