# PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English

arXiv: https://arxiv.org/abs/2212.10011 · HTML: https://mlsanity.com/paper/2212.10011/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 20 Dec 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [ELLE: Efficient Lifelong Pre-training for Emerging Data](https://mlsanity.com/paper/2203.06311/) (2022)
- [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://mlsanity.com/paper/1911.00841/) (2019)
- [Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning](https://mlsanity.com/paper/1802.02561/) (2018)
- [PolicyQA: A Reading Comprehension Dataset for Privacy Policies](https://mlsanity.com/paper/2010.02557/) (2020)
- [CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain](https://mlsanity.com/paper/2212.02974/) (2022)
- [Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset](https://mlsanity.com/paper/2008.09159/) (2020)
- [Intent Classification and Slot Filling for Privacy Policies](https://mlsanity.com/paper/2101.00123/) (2021)
- [Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies](https://mlsanity.com/paper/2204.08952/) (2022)

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

- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [SOS! Soft Prompt Attack Against Open-Source Large Language Models](https://mlsanity.com/paper/2407.03160/) (2024)
- [Benchmarking LLM Privacy Recognition for Social Robot Decision Making](https://mlsanity.com/paper/2507.16124/) (2025)
- [A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systems](https://mlsanity.com/paper/2506.02998/) (2025)

## Abstract (arXiv)

Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We evaluate several generic pre-trained language models and continue pre-training them on the collected corpus. We demonstrate that domain-specific continual pre-training offers performance improvements across all tasks.