Intent recognition is a fundamental component in task-oriented dialogue systems (TODS). Determining user intents and detecting whether an intent is Out-of-Scope (OOS) is crucial for TODS to provide reliable responses. However, traditional TODS require large amount of annotated data. In this work we propose a hybrid approach to combine BERT and LLMs in zero and few-shot settings to recognize intents and detect OOS utterances. Our approach leverages LLMs generalization power and BERT's computational efficiency in such scenarios. We evaluate our method on multi-party conversation corpora and observe that sharing information from BERT outputs to LLMs leads to system performance improvement.
@article{arxiv.2507.22289,
title = {Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations},
author = {Galo Castillo-López and Gaël de Chalendar and Nasredine Semmar},
journal= {arXiv preprint arXiv:2507.22289},
year = {2025}
}