# Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations

arXiv: https://arxiv.org/abs/2507.22289 · HTML: https://mlsanity.com/paper/2507.22289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Hyperspherical Variational Auto-Encoders](https://mlsanity.com/paper/1804.00891/) (2018)
- [MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations](https://mlsanity.com/paper/2403.10943/) (2024)
- [Intent Detection in the Age of LLMs](https://mlsanity.com/paper/2410.01627/) (2024)
- [HINT3: Raising the bar for Intent Detection in the Wild](https://mlsanity.com/paper/2009.13833/) (2020)
- [Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition](https://mlsanity.com/paper/2312.14667/) (2023)
- [Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering](https://mlsanity.com/paper/2308.15231/) (2023)
- [Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection](https://mlsanity.com/paper/2402.17256/) (2024)
- [Generate then Refine: Data Augmentation for Zero-shot Intent Detection](https://mlsanity.com/paper/2410.01953/) (2024)
- [A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks](https://mlsanity.com/paper/2410.13649/) (2024)
- [APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection](https://mlsanity.com/paper/2310.13380/) (2023)
- [Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval](https://mlsanity.com/paper/2301.10517/) (2023)

## Built on by (most cited first, 2 of 2)

- [How DDAIR you? Disambiguated Data Augmentation for Intent Recognition](https://mlsanity.com/paper/2601.11234/) (2026)
- [Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations](https://mlsanity.com/paper/2608.28926/) (2026)

## Abstract (arXiv)

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.