IntentGPT: Few-shot Intent Discovery with Large Language Models

TL;DR

IntentGPT leverages large language models with prompt engineering to discover new user intents in dialogue systems efficiently, requiring minimal labeled data and outperforming traditional data-intensive methods.

Contribution

We introduce IntentGPT, a training-free approach that uses prompt-based learning with LLMs for effective intent discovery without extensive data or fine-tuning.

Findings

Outperforms existing intent discovery methods on benchmark datasets.

Requires minimal labeled data and no model fine-tuning.

Effective in identifying emerging user intents in dialogue systems.

Abstract

In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is important to identify user's goals automatically to resolve their needs promptly. This has necessitated the integration of models that perform Intent Detection. However, users' intents are diverse and dynamic, making it challenging to maintain a fixed set of predefined intents. As a result, a more practical approach is to develop a model capable of identifying new intents as they emerge. We address the challenge of Intent Discovery, an area that has drawn significant attention in recent research efforts. Existing methods need to train on a substantial amount of data for correctly identifying new intents, demanding significant human effort. To overcome this, we introduce IntentGPT, a novel training-free method that effectively prompts Large Language Models (LLMs) such as GPT-4 to discover new intents with minimal labeled data. IntentGPT comprises an \textit{In-Context Prompt Generator}, which generates informative prompts for In-Context Learning, an \textit{Intent Predictor} for classifying and discovering user intents from utterances, and a \textit{Semantic Few-Shot Sampler} that selects relevant few-shot examples and a set of known intents to be injected into the prompt. Our experiments show that IntentGPT outperforms previous methods that require extensive domain-specific data and fine-tuning, in popular benchmarks, including CLINC and BANKING, among others.