Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918107596980224 |
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| author | Castillo-López, Galo de Chalendar, Gaël Semmar, Nasredine |
| author_facet | Castillo-López, Galo de Chalendar, Gaël Semmar, Nasredine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations Castillo-López, Galo de Chalendar, Gaël Semmar, Nasredine Computation and Language Machine Learning 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. |
| title | Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2507.22289 |