# How DDAIR you? Disambiguated Data Augmentation for Intent Recognition

arXiv: https://arxiv.org/abs/2601.11234 · HTML: https://mlsanity.com/paper/2601.11234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [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)
- [Understanding Dataset Difficulty with V-Usable Information](https://mlsanity.com/paper/2110.08420/) (2021)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Intent Detection in the Age of LLMs](https://mlsanity.com/paper/2410.01627/) (2024)
- [Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information](https://mlsanity.com/paper/2302.05096/) (2023)
- [IDAS: Intent Discovery with Abstractive Summarization](https://mlsanity.com/paper/2305.19783/) (2023)
- [Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering](https://mlsanity.com/paper/2308.15231/) (2023)
- [Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues](https://mlsanity.com/paper/2412.09049/) (2024)
- [Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing](https://mlsanity.com/paper/2104.12114/) (2021)
- [LLM-based Semantic Augmentation for Harmful Content Detection](https://mlsanity.com/paper/2504.15548/) (2025)
- [Generate then Refine: Data Augmentation for Zero-shot Intent Detection](https://mlsanity.com/paper/2410.01953/) (2024)
- [Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification](https://mlsanity.com/paper/2412.15603/) (2024)
- [Domain Adaptation in Intent Classification Systems: A Review](https://mlsanity.com/paper/2404.14415/) (2024)
- [Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations](https://mlsanity.com/paper/2507.22289/) (2025)

## Built on by (most cited first, 1 of 1)

- [Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations](https://mlsanity.com/paper/2608.28926/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are effective for data augmentation in classification tasks like intent detection. In some cases, they inadvertently produce examples that are ambiguous with regard to untargeted classes. We present DDAIR (Disambiguated Data Augmentation for Intent Recognition) to mitigate this problem. We use Sentence Transformers to detect ambiguous class-guided augmented examples generated by LLMs for intent recognition in low-resource scenarios. We identify synthetic examples that are semantically more similar to another intent than to their target one. We also provide an iterative re-generation method to mitigate such ambiguities. Our findings show that sentence embeddings effectively help to (re)generate less ambiguous examples, and suggest promising potential to improve classification performance in scenarios where intents are loosely or broadly defined.