Curate-Train-Refine: A Closed-Loop Agentic Framework for Zero Shot Classification
This work addresses the high inference cost and latency that hinder the deployment of large language models (LLMs) in zero-shot text classification. To overcome this bottleneck, the authors propose a closed-loop agent framework that leverages the LLM as an intelligent data curator. Within this framework, the LLM dynamically generates training examples, performs error analysis on a lightweight classifier, and synthesizes targeted samples to iteratively refine the training data. Evaluated on four mainstream benchmarks, the approach significantly outperforms standard zero-shot and few-shot baselines, achieving high accuracy while substantially improving inference efficiency. This enables practical, deployable zero-shot classification without sacrificing performance.