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Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
arXiv – CS AI|Fengzhi Li, Liang Zhang, Yuan Zuo, Ruiqing Zhao, YanSong Liu, Yunfei Ma, Fanyu Meng, Junlan Feng||1 views
🤖AI Summary
Researchers introduce GraphSSR, a new framework that improves zero-shot graph learning by combining Large Language Models with adaptive subgraph denoising. The system addresses structural noise issues in existing methods through a dynamic 'Sample-Select-Reason' pipeline and reinforcement learning training.
Key Takeaways
- →GraphSSR solves the 'one-size-fits-all' problem in existing LLM-based graph reasoning by introducing adaptive subgraph extraction.
- →The framework uses a Sample-Select-Reason pipeline to dynamically filter out irrelevant graph data and reduce structural noise.
- →SSR-SFT data synthesis strategy generates high-quality training data for supervised fine-tuning of language models on graph tasks.
- →Two-stage reinforcement learning framework (SSR-RL) explicitly optimizes sampling and selection operations for better graph denoising.
- →The approach enables better zero-shot generalization to unseen domains without traditional Graph Neural Network architectural dependencies.
#machine-learning#graph-neural-networks#large-language-models#zero-shot-learning#reinforcement-learning#graph-reasoning#subgraph-extraction#denoising#supervised-fine-tuning
Read Original →via arXiv – CS AI
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