@inproceedings{castillo-lopez-etal-2025-intent,
    title = "Intent Recognition and Out-of-Scope Detection using {LLM}s in Multi-party Conversations",
    author = "Castillo-L{\'o}pez, Galo  and
      de Chalendar, Gael  and
      Semmar, Nasredine",
    editor = "B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Lef{\`e}vre, Fabrice  and
      Asher, Nicholas  and
      Kim, Seokhwan  and
      Merlin, Teva",
    booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2025",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-orcids-2023-acl/2025.sigdial-1.41/",
    pages = "504--512",
    abstract = "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 scenarios 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 lead to system performance improvement."
}