GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification

Understand

Text classification is vital for Web for Good applications like hate speech and misinformation detection.

• However, traditional models (e.g., BERT) often fail in dynamic few-shot settings where labeled data are scarce, and target labels frequently evolve.
• While Large Language Models (LLMs) show promise in few-shot settings, their performance is often hindered by increased input size in dynamic evolving scenarios.
• To address these issues, we propose GORAG, a Graph-based Online Retrieval-Augmented Generation framework for dynamic few-shot text classification.