# HINT3: Raising the bar for Intent Detection in the Wild

arXiv: https://arxiv.org/abs/2009.13833 · HTML: https://mlsanity.com/paper/2009.13833/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 21 of 21)

- [Effectiveness of Pre-training for Few-shot Intent Classification](https://mlsanity.com/paper/2109.05782/) (2021)
- [Intent Detection in the Age of LLMs](https://mlsanity.com/paper/2410.01627/) (2024)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification](https://mlsanity.com/paper/2412.15603/) (2024)
- [Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training](https://mlsanity.com/paper/2306.05278/) (2023)
- [Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy](https://mlsanity.com/paper/2205.11966/) (2022)
- [Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants](https://mlsanity.com/paper/2301.06544/) (2023)
- [Domain Adaptation in Intent Classification Systems: A Review](https://mlsanity.com/paper/2404.14415/) (2024)
- [Fast and Light-Weight Answer Text Retrieval in Dialogue Systems](https://mlsanity.com/paper/2205.14226/) (2022)
- [Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations](https://mlsanity.com/paper/2507.22289/) (2025)
- [Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks](https://mlsanity.com/paper/2205.09021/) (2022)
- [Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval](https://mlsanity.com/paper/2301.10517/) (2023)
- [Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing](https://mlsanity.com/paper/2507.01541/) (2025)
- [IntentGrasp: A Comprehensive Benchmark for Intent Understanding](https://mlsanity.com/paper/2605.06832/) (2026)
- [Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis](https://mlsanity.com/paper/2310.05374/) (2023)
- [Minimizing PLM-Based Few-Shot Intent Detectors](https://mlsanity.com/paper/2407.09943/) (2024)
- [Intent Classification on Low-Resource Languages with Query Similarity Search](https://mlsanity.com/paper/2505.18241/) (2025)
- [Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021](https://mlsanity.com/paper/2606.31081/) (2026)
- [Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models](https://mlsanity.com/paper/2607.27421/) (2026)

## Abstract (arXiv)

Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations. To facilitate benchmarking which can reflect near real-world scenarios, we introduce 3 new datasets created from live chatbots in diverse domains. Unlike most existing datasets that are crowdsourced, our datasets contain real user queries received by the chatbots and facilitates penalising unwanted correlations grasped during the training process. We evaluate 4 NLU platforms and a BERT based classifier and find that performance saturates at inadequate levels on test sets because all systems latch on to unintended patterns in training data.