Enhancing Intent Classifier Training with Large Language Model-generated Data

Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.