Improving Unsupervised Zero-Shot Intent Classification with Dynamic Candidate Selection

Task-oriented dialogue systems allow users to interact through natural language with a variety of digital devices in order to accomplish some goal, within which intent classification is an integral component in ensuring the satisfaction of a user’s request. Applications of Large Language Models (LLMs) in this domain can suffer from prohibitively high computation requirements and costs owing to the number of input tokens scaling with the number of intents. We propose a framework using candidate selection, aimed at refining a model’s selection of candidate intents to reduce inference costs.