# FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score

arXiv: https://arxiv.org/abs/2310.05083 · HTML: https://mlsanity.com/paper/2310.05083/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (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)
- [OpenOOD: Benchmarking Generalized Out-of-Distribution Detection](https://mlsanity.com/paper/2210.07242/) (2022)
- [Continual Pre-training of Language Models](https://mlsanity.com/paper/2302.03241/) (2023)
- [Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder](https://mlsanity.com/paper/2003.02977/) (2020)
- [How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?](https://mlsanity.com/paper/2203.04450/) (2022)
- [Types of Out-of-Distribution Texts and How to Detect Them](https://mlsanity.com/paper/2109.06827/) (2021)
- [Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog](https://mlsanity.com/paper/1912.12800/) (2019)
- [Continual Training of Language Models for Few-Shot Learning](https://mlsanity.com/paper/2210.05549/) (2022)
- [Unsupervised Out-of-Domain Detection via Pre-trained Transformers](https://mlsanity.com/paper/2106.00948/) (2021)
- [A Survey on Out-of-Distribution Detection in NLP](https://mlsanity.com/paper/2305.03236/) (2023)
- [Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features](https://mlsanity.com/paper/2301.12715/) (2023)
- [Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection](https://mlsanity.com/paper/2305.16966/) (2023)
- [Falsehoods that ML researchers believe about OOD detection](https://mlsanity.com/paper/2210.12767/) (2022)
- [Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation](https://mlsanity.com/paper/2004.08333/) (2020)

## Built on by (most cited first, 5 of 5)

- [Shh, don't say that! Domain Certification in LLMs](https://mlsanity.com/paper/2502.19320/) (2025)
- [Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression](https://mlsanity.com/paper/2407.13141/) (2024)
- [Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks](https://mlsanity.com/paper/2508.17690/) (2025)
- [Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classification](https://mlsanity.com/paper/2408.11237/) (2024)
- [One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning](https://mlsanity.com/paper/2407.09011/) (2024)

## Abstract (arXiv)

Detecting out-of-distribution (OOD) instances is crucial for NLP models in practical applications. Although numerous OOD detection methods exist, most of them are empirical. Backed by theoretical analysis, this paper advocates for the measurement of the "OOD-ness" of a test case boldsymbolx through the likelihood ratio between out-distribution mathcal P_textitout and in-distribution mathcal P_textitin. We argue that the state-of-the-art (SOTA) feature-based OOD detection methods, such as Maha and KNN, are suboptimal since they only estimate in-distribution density p_textitin(boldsymbolx). To address this issue, we propose FLatS, a principled solution for OOD detection based on likelihood ratio. Moreover, we demonstrate that FLatS can serve as a general framework capable of enhancing other OOD detection methods by incorporating out-distribution density p_textitout(boldsymbolx) estimation. Experiments show that FLatS establishes a new SOTA on popular benchmarks. Our code is publicly available at https://github.com/linhaowei1/FLatS.