Enhancing intent detection through ChatGPT-Driven data augmentation

Abstract

The fields of Artificial Intelligence (AI) and Natural Language Processing (NLP) have advanced recently allowing for the development of various automated applica tions such as chatbots. Intent detection is an important part of conversational agent work, which aims to recognize and classify user intentions for smart and responsive interactions [1]. Recently, large language models (LLMs), such as OpenAI’s GPT 4, revealed new opportunities for processing and understanding natural language. However, robust intent detection systems still face the scarcity problem and the lack of diversity in training data. AI models often need big datasets to adequately represent the full range of expressions, dialects, and subtilities of human language for correct understanding and classification of user intents. It is a time-consuming and labor intensive task to create such datasets, especially when focusing on specific application domains or low-resource languages. This bottleneck significantly hampers the devel opment and scalability of intent detection systems, especially in specialized fields or for non-mainstream languages. Data augmentation, the process of artificially expanding the training dataset by generating new data samples, presents a promising solution to this challenge [2]. By creating varied and synthetic training examples, data augmentation can improve the generalization capabilities of AI models and enhance their performance, especially in scenarios with limited data. However, traditional data augmentation techniques in NLP, such as synonym replacement or back-translation, often fall short of maintaining the contextual and semantic coherence necessary for high-quality intent detection. The emergence of generative pre-trained large language models has transformed the landscape of data augmentation. Despite the abundance of such models (which excel in understanding human language and context and demonstrate the potential to generate linguistically rich and diverse text samples), our focus is on OpenAI’s GPT (see a review in [3]). The rationale behind this choice is clear: while most of these models support only English and/or other widely spoken languages, ChatGPT [4] stands out for its capability to generate content in multiple languages. This capability can be harnessed to augment training datasets for intent detection tasks, providing a wealth of contextually relevant, varied, and syntactically correct data samples [5]. This paper seeks to investigate the effectiveness of ChatGPT for data augmentation within the realm of intent detection. Our contributions have three main aspects: • An approach to augment data utilizing ChatGPT to enhance the training dataset for intent detection-based tasks. • Comprehensive experiments to evaluate the influence of ChatGPT-generated augmentation on the performance of intent detection models. Our focus is particularly on domain-specific scenarios and languages with limited data resources. • An in-depth analysis of the augmented data’s quality, considering the linguistic diversity, contextual relevance, and its impact on the robustness of the intent detection systems