# Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

arXiv: https://arxiv.org/abs/2302.05096 · HTML: https://mlsanity.com/paper/2302.05096/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 62 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 30 of 39)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Understanding Dataset Difficulty with V-Usable Information](https://mlsanity.com/paper/2110.08420/) (2021)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt](https://mlsanity.com/paper/2206.07137/) (2022)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://mlsanity.com/paper/1903.05566/) (2019)
- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue](https://mlsanity.com/paper/2009.13570/) (2020)
- [A Deep Generative Framework for Paraphrase Generation](https://mlsanity.com/paper/1709.05074/) (2017)
- [Neural Data Augmentation via Example Extrapolation](https://mlsanity.com/paper/2102.01335/) (2021)
- [Few-Shot Semantic Parsing with Language Models Trained On Code](https://mlsanity.com/paper/2112.08696/) (2021)
- [Data Augmentation for Intent Classification with Off-the-shelf Large Language Models](https://mlsanity.com/paper/2204.01959/) (2022)
- [Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference](https://mlsanity.com/paper/2010.13009/) (2020)
- [Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning](https://mlsanity.com/paper/2109.06349/) (2021)
- [A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification](https://mlsanity.com/paper/1910.04176/) (2019)

## Built on by (most cited first, 24 of 24)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024)
- [Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges](https://mlsanity.com/paper/2403.02990/) (2024)
- [PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation](https://mlsanity.com/paper/2310.14192/) (2023)
- [See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses](https://mlsanity.com/paper/2408.08978/) (2024)
- [Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification](https://mlsanity.com/paper/2402.07214/) (2024)
- [IntentGPT: Few-shot Intent Discovery with Large Language Models](https://mlsanity.com/paper/2411.10670/) (2024)
- [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)
- [Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models](https://mlsanity.com/paper/2309.10707/) (2023)
- [InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2310.08885/) (2023)
- [Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition](https://mlsanity.com/paper/2311.09552/) (2023)
- [ICL Markup: Structuring In-Context Learning using Soft-Token Tags](https://mlsanity.com/paper/2312.07405/) (2023)
- [ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler](https://mlsanity.com/paper/2403.17536/) (2024)
- [Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information](https://mlsanity.com/paper/2410.12774/) (2024)
- [Dense Retrieval as Indirect Supervision for Large-space Decision Making](https://mlsanity.com/paper/2310.18619/) (2023)
- [When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes](https://mlsanity.com/paper/2404.12365/) (2024)
- [IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery](https://mlsanity.com/paper/2310.16761/) (2023)
- [GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection](https://mlsanity.com/paper/2508.17057/) (2025)
- [How DDAIR you? Disambiguated Data Augmentation for Intent Recognition](https://mlsanity.com/paper/2601.11234/) (2026)
- [Measuring Pointwise V-Usable Information In-Context-ly](https://mlsanity.com/paper/2310.12300/) (2023)
- [TARDiS : Text Augmentation for Refining Diversity and Separability](https://mlsanity.com/paper/2501.02739/) (2025)
- [ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification](https://mlsanity.com/paper/2502.05923/) (2025)
- [Intent Classification on Low-Resource Languages with Query Similarity Search](https://mlsanity.com/paper/2505.18241/) (2025)

## Abstract (arXiv)

This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).