# ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection

arXiv: https://arxiv.org/abs/2308.13517 · HTML: https://mlsanity.com/paper/2308.13517/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition](https://mlsanity.com/paper/2110.15063/) (2021)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery](https://mlsanity.com/paper/2210.08909/) (2022)
- [UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning](https://mlsanity.com/paper/2210.10722/) (2022)

## Built on by (most cited first, 9 of 9)

- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model](https://mlsanity.com/paper/2407.01245/) (2024)
- [Generate then Refine: Data Augmentation for Zero-shot Intent Detection](https://mlsanity.com/paper/2410.01953/) (2024)
- [On Evaluation Protocols for Data Augmentation in a Limited Data Scenario](https://mlsanity.com/paper/2402.14895/) (2024)
- [Compositional Generalization for Data-to-Text Generation](https://mlsanity.com/paper/2312.02748/) (2023)
- [Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language](https://mlsanity.com/paper/2409.00061/) (2024)
- [Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of English-Japanese Biomedical Adaptation](https://mlsanity.com/paper/2510.12115/) (2025)

## Abstract (arXiv)

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.