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    <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.</abstract>
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%0 Conference Proceedings
%T Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
%A Castillo-López, Galo
%A de Chalendar, Gael
%A Semmar, Nasredine
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F castillo-lopez-etal-2025-intent
%X 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.
%U https://aclanthology.org/2025.sigdial-1.41/
%P 504-512

Markdown (Informal)

[Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations](https://aclanthology.org/2025.sigdial-1.41/) (Castillo-López et al., SIGDIAL 2025)

• Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations (Castillo-López et al., SIGDIAL 2025)

ACL

• Galo Castillo-López, Gael de Chalendar, and Nasredine Semmar. 2025. Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 504–512, Avignon, France. Association for Computational Linguistics.