AI RESEARCH
Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models
arXiv CS.CL
•
ArXi:2501.17549v2 Announce Type: replace Graph-structured data plays a vital role in numerous domains, such as social networks, citation networks, commonsense reasoning graphs and knowledge graphs. While graph neural networks have been employed for graph processing, recent advancements have explored integrating large language models for graph-based tasks. In this paper, we propose a novel approach named Learnable Graph Pooling Token (LGPT), which addresses the limitations of the scalability issues in node-level projection and information loss in graph-level projection.