From Intent Discovery to Recognition with Topic Modeling and Synthetic Data (2505.11176v1)

Abstract: Understanding and recognizing customer intents in AI systems is crucial, particularly in domains characterized by short utterances and the cold start problem, where recommender systems must include new products or services without sufficient real user data. Customer utterances are characterized by infrequent word co-occurences and high term variability, which poses significant challenges for traditional methods in specifying distinct user needs and preparing synthetic queries. To address this, we propose an agentic LLM framework for topic modeling and synthetic query generation, which accelerates the discovery and recognition of customer intents. We first apply hierarchical topic modeling and intent discovery to expand a human-curated taxonomy from 36 generic user intents to 278 granular intents, demonstrating the potential of LLMs to significantly enhance topic specificity and diversity. Next, to support newly discovered intents and address the cold start problem, we generate synthetic user query data, which augments real utterances and reduces dependency on human annotation, especially in low-resource settings. Topic model experiments show substantial improvements in coherence and relevance after topic expansion, while synthetic data experiments indicate that in-class few-shot prompting significantly improves the quality and utility of synthetic queries without compromising diversity. We also show that LLM-generated intent descriptions and keywords can effectively substitute for human-curated versions when used as context for synthetic query generation. Our research underscores the scalability and utility of LLM agents in topic modeling and highlights the strategic use of synthetic utterances to enhance dataset variability and coverage for intent recognition. We present a comprehensive and robust framework for online discovery and recognition of new customer intents in dynamic domains.