ChatGPT as Data Augmentation for Compositional Generalization: A Case
  Study in Open Intent Detection
Yihao Fang Xianzhi Li Stephen W. Thomas Xiaodan Zhu
Open intent detection, a crucial aspect of natural language understanding,
involves the identification of previously unseen intents in user-generated
text. Despite the progress made in this field, challenges persist in handling
new combinations of language components, which is essential for compositional
generalization. In this paper, we present a case study exploring the use of
ChatGPT as a data augmentation technique to enhance compositional
generalization in open intent detection tasks. We begin by discussing the
limitations of existing benchmarks in evaluating this problem, highlighting the
need for constructing datasets for addressing compositional generalization in
open intent detection tasks. By incorporating synthetic data generated by
ChatGPT into the training process, we demonstrate that our approach can
effectively improve model performance. Rigorous evaluation of multiple
benchmarks reveals that our method outperforms existing techniques and
significantly enhances open intent detection capabilities. Our findings
underscore the potential of large language models like ChatGPT for data
augmentation in natural language understanding tasks.