# BED: Bi-Encoder-Based Detectors for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/2306.08852 · HTML: https://mlsanity.com/paper/2306.08852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 29 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [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)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [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)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection](https://mlsanity.com/paper/2101.03778/) (2021)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Out-of-domain Detection for Natural Language Understanding in Dialog Systems](https://mlsanity.com/paper/1909.03862/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [Out-of-Domain Detection for Low-Resource Text Classification Tasks](https://mlsanity.com/paper/1909.05357/) (2019)
- [Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog](https://mlsanity.com/paper/1912.12800/) (2019)
- [Unsupervised Out-of-Domain Detection via Pre-trained Transformers](https://mlsanity.com/paper/2106.00948/) (2021)
- [Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems](https://mlsanity.com/paper/1807.11567/) (2018)
- [PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation](https://mlsanity.com/paper/2111.00506/) (2021)
- [A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery](https://mlsanity.com/paper/2303.04134/) (2023)

## Abstract (arXiv)

This paper introduces a novel method leveraging bi-encoder-based detectors along with a comprehensive study comparing different out-of-distribution (OOD) detection methods in NLP using different feature extractors. The feature extraction stage employs popular methods such as Universal Sentence Encoder (USE), BERT, MPNET, and GLOVE to extract informative representations from textual data. The evaluation is conducted on several datasets, including CLINC150, ROSTD-Coarse, SNIPS, and YELLOW. Performance is assessed using metrics such as F1-Score, MCC, FPR@90, FPR@95, AUPR, an AUROC. The experimental results demonstrate that the proposed bi-encoder-based detectors outperform other methods, both those that require OOD labels in training and those that do not, across all datasets, showing great potential for OOD detection in NLP. The simplicity of the training process and the superior detection performance make them applicable to real-world scenarios. The presented methods and benchmarking metrics serve as a valuable resource for future research in OOD detection, enabling further advancements in this field. The code and implementation details can be found on our GitHub repository: https://github.com/yellowmessenger/ood-detection.