Our paper, “Intent Recognition and Out-of Scope Detection using LLMs in Multi-party Conversations”, was presented at the Proceedings of The 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2025).
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 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.
Authors
Galo Castillo-López, Gaël de Chalendar, Nasredine Semmar
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