Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents
This article explores the gaps that can manifest when using a large language model (LLM) to obtain simplified interpretations of data practices from a complex privacy policy. We exemplify these gaps to showcase issues in accuracy, completeness, clarity and representation, while advocating for continued research to realize an LLM's true potential in revolutionizing privacy management through personal assistants and automated compliance checking.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingLarge Language ModelManagementSimilar Papers 제목 키워드 기반
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
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 retrie…
Question AnsweringReading ComprehensionEnhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations
Machine Reading Comprehension (MRC), which requires a machine to answer questions given the relevant documents, is an important way to test machines’ ability to understand human language. Multiple-choice MRC is one of th…
AttributeMachine Reading ComprehensionMultiple-choiceReading ComprehensionMedical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task ca…
MedQAQuestion AnsweringReading ComprehensionCHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research Labs
University research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it …
Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents
This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent. We demonstrate that LLMs significantly outperform tradition…
ManagementQuestion Answering