# PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation

arXiv: https://arxiv.org/abs/2111.00506 · HTML: https://mlsanity.com/paper/2111.00506/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem](https://mlsanity.com/paper/1812.05720/) (2018)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration](https://mlsanity.com/paper/1912.07458/) (2019)
- [Complexity of Word Collocation Networks: A Preliminary Structural Analysis](https://mlsanity.com/paper/1310.5111/) (2013)

## Built on by (most cited first, 12 of 12)

- [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://mlsanity.com/paper/2209.15558/) (2022)
- [A Survey on Out-of-Distribution Detection in NLP](https://mlsanity.com/paper/2305.03236/) (2023)
- [Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection](https://mlsanity.com/paper/2305.13282/) (2023)
- [OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries](https://mlsanity.com/paper/2211.12850/) (2022)
- [Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks](https://mlsanity.com/paper/2307.07002/) (2023)
- [Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration](https://mlsanity.com/paper/2506.22819/) (2025)
- [Semantic Role Labeling Guided Out-of-distribution Detection](https://mlsanity.com/paper/2305.18026/) (2023)
- [Constraining Representations Yields Models That Know What They Don't Know](https://mlsanity.com/paper/2208.14488/) (2022)
- [BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection](https://mlsanity.com/paper/2306.08852/) (2023)
- [Tree-based Ensemble Learning for Out-of-distribution Detection](https://mlsanity.com/paper/2405.03060/) (2024)
- [Enhancing Deep Neural Network Reliability with Refinement and Calibration](https://mlsanity.com/paper/2605.23249/) (2026)
- [Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance](https://mlsanity.com/paper/2609.00961/) (2026)

## Abstract (arXiv)

While Out-of-distribution (OOD) detection has been well explored in computer vision, there have been relatively few prior attempts in OOD detection for NLP classification. In this paper we argue that these prior attempts do not fully address the OOD problem and may suffer from data leakage and poor calibration of the resulting models. We present PnPOOD, a data augmentation technique to perform OOD detection via out-of-domain sample generation using the recently proposed Plug and Play Language Model (Dathathri et al., 2020). Our method generates high quality discriminative samples close to the class boundaries, resulting in accurate OOD detection at test time. We demonstrate that our model outperforms prior models on OOD sample detection, and exhibits lower calibration error on the 20 newsgroup text and Stanford Sentiment Treebank dataset (Lang, 1995; Socheret al., 2013). We further highlight an important data leakage issue with datasets used in prior attempts at OOD detection, and share results on a new dataset for OOD detection that does not suffer from the same problem.