mlsanityData to 7 Oct 2026

PolicyQA: A Reading Comprehension Dataset for Privacy Policies

arXiv 2010.02557 ↗First posted 6 Oct 2020cs.CLcited by 31

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Abstract (arXiv)

Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 5; 2024: 6; 2025: 8; 2026: 5.

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  1. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  2. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  3. 2021ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answerscited by 29
  4. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 29
  5. 2025Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunitiescited by 21
  6. 2021Intent Classification and Slot Filling for Privacy Policiescited by 18
  7. 2023VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Modelscited by 15
  8. 2023Bringing order into the realm of Transformer-based language models for artificial intelligence and lawcited by 13
  9. 2024Retrieval-Augmented Data Augmentation for Low-Resource Domain Taskscited by 12
  10. 2022Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policiescited by 10
  11. 2023Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challengescited by 9
  12. 2024Empowering Users in Digital Privacy Management through Interactive LLM-Based Agentscited by 9
19 more
  1. 2021Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Readingcited by 7
  2. 2021Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approachcited by 5
  3. 2022PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in Englishcited by 5
  4. 2024GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answeringcited by 4
  5. 2025Benchmarking LLM Privacy Recognition for Social Robot Decision Makingcited by 3
  6. 2025A Multi-Agent Framework for Mitigating Dialect Biases in Privacy Policy Question-Answering Systemscited by 2
  7. 2025A Longitudinal Measurement of Privacy Policy Evolution for Large Language Modelscited by 2
  8. 2024Automated Detection and Analysis of Data Practices Using A Real-World Corpuscited by 1
  9. 2025Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scalecited by 1
  10. 2025Overcoming Language Barriers: Multilingual Analysis of the 2023 Swiss Privacy Law's Impactcited by 1
  11. 2026Knowledge Graph Representations for LLM-Based Policy Compliance Reasoningcited by 1
  12. 2023Exploring the State of the Art in Legal QA Systemscited by 0
  13. 2023Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&Acited by 0
  14. 2024Demystifying Legalese: An Automated Approach for Summarizing and Analyzing Overlaps in Privacy Policies and Terms of Servicecited by 0
  15. 2025An LLM-enabled semantic-centric framework to consume privacy policiescited by 0
  16. 2026Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QAcited by 0
  17. 2026APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretationcited by 0
  18. 2026Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documentscited by 0
  19. 2026Which Rules Matter Now? Policy-Centroid Routing Before an Intelligent System Actscited by 0