用大模型自动生成文本数据，提升分类模型性能
AutoGeTS: Knowledge-based Automated Generation of Text Synthetics for Improving Text Classification
- 基于大模型生成合成数据，自动筛选有效输入以优化生成效果
- 集成多种搜索策略，实验表明其在多类任务中表现更优
- 适合数据稀缺场景下的文本分类模型增强，尤其适用于小样本
在实际应用中开发文本分类模型时，一个主要挑战是难以为所有文本类别收集足够数据。本文利用大语言模型（LLMs）生成合成数据，并使用这些数据提升模型性能，而无需等待更多真实数据的采集与标注。由于大模型对不同输入会生成不同合成数据，我们设计了一种自动化流程，旨在搜索能产生更‘有效’合成数据的输入样本。通过大量实验评估三种搜索策略，并基于实验结果构建集成算法，根据类别特征动态选择最优策略。进一步实验表明，该集成方法在使用大模型生成合成数据以改进分类模型方面，优于单一策略。
原文摘要 · Abstract (English)
When developing text classification models for real world applications, one major challenge is the difficulty to collect sufficient data for all text classes. In this work, we address this challenge by utilizing large language models (LLMs) to generate synthetic data and using such data to improve the performance of the models without waiting for more real data to be collected and labelled. As an LLM generates different synthetic data in response to different input examples, we formulate an automated workflow, which searches for input examples that lead to more ``effective'' synthetic data for improving the model concerned. We study three search strategies with an extensive set of experiments, and use experiment results to inform an ensemble algorithm that selects a search strategy according to the characteristics of a class. Our further experiments demonstrate that this ensemble approach is more effective than each individual strategy in our automated workflow for improving classification models using LLMs.
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