Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification
<|reference_start|>Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification: Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses. Although out-of-scope (OOS) detection for intent classifiers has been studied, previous work has not yet studied changes in classifier performance against hard-negative out-of-scope utterances (i.e., inputs that share common features with in-scope data, but are actually out-of-scope). We present an automated technique to generate hard-negative OOS data using ChatGPT. We use our technique to build five new hard-negative OOS datasets, and evaluate each against three benchmark intent classifiers. We show that classifiers struggle to correctly identify hard-negative OOS utterances more than general OOS utterances. Finally, we show that incorporating hard-negative OOS data for training improves model robustness when detecting hard-negative OOS data and general OOS data. Our technique, datasets, and evaluation address an important void in the field, offering a straightforward and inexpensive way to collect hard-negative OOS data and improve intent classifiers' robustness.<|reference_end|>
@article{li2024generating,
    title={Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent
  Classification},
    author={Zhijian Li, Stefan Larson, Kevin Leach},
    journal={arXiv preprint arXiv:2403.05640},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05640},
    primaryClass={cs.CL}
}