用大模型生成+小模型精修，提升零样本意图识别数据质量
Generate then Refine: Data Augmentation for Zero-shot Intent Detection
- 先用大模型零样本生成语句，再用小模型优化
- 在未见领域上显著提升数据多样性和可用性
- 适合资源匮乏场景下的意图识别系统构建
本文提出一种面向零资源领域意图检测的数据增强方法。现有方法依赖每个意图类别的少量标注样本，但在意图种类繁多的场景下成本高昂。我们采用两阶段策略：首先使用开源大语言模型在零样本设置下生成意图对应的语句；其次设计一个更小的序列到序列模型（Refiner），用于优化生成语句。Refiner 在已见领域上微调后，应用于未见领域。通过在生成数据上训练意图分类器，并在真实人类数据上评估，结果表明，Refiner 显著提升了未见领域的数据实用性和多样性，优于零样本大模型基线及常见基线方法。实验表明，大模型零样本生成结合小模型精修的两步策略，可为意图检测提供高质量数据。
原文摘要 · Abstract (English)
In this short paper we propose a data augmentation method for intent detection in zero-resource domains. Existing data augmentation methods rely on few labelled examples for each intent category, which can be expensive in settings with many possible intents. We use a two-stage approach: First, we generate utterances for intent labels using an open-source large language model in a zero-shot setting. Second, we develop a smaller sequence-to-sequence model (the Refiner), to improve the generated utterances. The Refiner is fine-tuned on seen domains and then applied to unseen domains. We evaluate our method by training an intent classifier on the generated data, and evaluating it on real (human) data. We find that the Refiner significantly improves the data utility and diversity over the zero-shot LLM baseline for unseen domains and over common baseline approaches. Our results indicate that a two-step approach of a generative LLM in zero-shot setting and a smaller sequence-to-sequence model can provide high-quality data for intent detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。