用大模型自动优化小模型训练数据，提升零样本分类准确率。
Curate-Train-Refine: A Closed-Loop Agentic Framework for Zero Shot Classification
- 大模型生成并迭代优化小模型的训练数据
- 在4个基准上超越标准零/少样本方法
- 适合资源有限但需高精度分类的场景
大型语言模型（LLMs）和高容量编码器推动了零样本和少样本分类的发展，但其推理成本和延迟限制了实际部署。我们提出一种利用大模型动态生成监督信号来训练轻量级文本分类器的方法。该方法采用迭代式智能体闭环，由大模型负责数据筛选、分析模型成功与失败案例，并合成针对性样例以修正错误。这一闭环生成与评估过程持续提升数据质量，并适配下游分类器与任务需求。在四个广泛使用的基准测试中，该方法始终优于标准的零样本和少样本基线。结果表明，大模型可有效充当数据策展者，实现高精度且低运营成本的分类。
原文摘要 · Abstract (English)
Large language models (LLMs) and high-capacity encoders have advanced zero and few-shot classification, but their inference cost and latency limit practical deployment. We propose training lightweight text classifiers using dynamically generated supervision from an LLM. Our method employs an iterative, agentic loop in which the LLM curates training data, analyzes model successes and failures, and synthesizes targeted examples to address observed errors. This closed-loop generation and evaluation process progressively improves data quality and adapts it to the downstream classifier and task. Across four widely used benchmarks, our approach consistently outperforms standard zero and few-shot baselines. These results indicate that LLMs can serve effectively as data curators, enabling accurate and efficient classification without the operational cost of large-model deployment.
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