Text2Graph: Improving Text Classification in Data-Poor Environments with LLMs and GNNs
Published:Dec 10, 2025 20:31
•1 min read
•ArXiv
Analysis
This research introduces Text2Graph, a promising approach to enhance text classification performance, particularly in scenarios where labeled data is limited. The integration of lightweight Language Models (LLMs) and Graph Neural Networks (GNNs) presents a novel and potentially effective solution.
Key Takeaways
- •Combines lightweight LLMs and GNNs for text classification.
- •Addresses the challenge of text classification with limited labeled data.
- •Presented on ArXiv, indicating early-stage research.
Reference
“The study focuses on using lightweight LLMs and GNNs.”