Privacy Policy Analysis through Prompt Engineering for LLMs

• URL: http://arxiv.org/abs/2409.14879v1
• Date: Mon, 23 Sep 2024 10:23:31 GMT
• Title: Privacy Policy Analysis through Prompt Engineering for LLMs
• Authors: Arda Goknil, Femke B. Gelderblom, Simeon Tverdal, Shukun Tokas, Hui Song,
• Abstract summary: PAPEL (Privacy Policy Analysis through Prompt Engineering for LLMs) is a framework harnessing the power of Large Language Models (LLMs) to automate the analysis of privacy policies. It aims to streamline the extraction, annotation, and summarization of information from these policies, enhancing their accessibility and comprehensibility without requiring additional model training. We demonstrate the effectiveness of PAPEL with two applications: (i) annotation and (ii) contradiction analysis.
• Score: 3.059256166047627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Privacy policies are often obfuscated by their complexity, which impedes transparency and informed consent. Conventional machine learning approaches for automatically analyzing these policies demand significant resources and substantial domain-specific training, causing adaptability issues. Moreover, they depend on extensive datasets that may require regular maintenance due to changing privacy concerns. In this paper, we propose, apply, and assess PAPEL (Privacy Policy Analysis through Prompt Engineering for LLMs), a framework harnessing the power of Large Language Models (LLMs) through prompt engineering to automate the analysis of privacy policies. PAPEL aims to streamline the extraction, annotation, and summarization of information from these policies, enhancing their accessibility and comprehensibility without requiring additional model training. By integrating zero-shot, one-shot, and few-shot learning approaches and the chain-of-thought prompting in creating predefined prompts and prompt templates, PAPEL guides LLMs to efficiently dissect, interpret, and synthesize the critical aspects of privacy policies into user-friendly summaries. We demonstrate the effectiveness of PAPEL with two applications: (i) annotation and (ii) contradiction analysis. We assess the ability of several LLaMa and GPT models to identify and articulate data handling practices, offering insights comparable to existing automated analysis approaches while reducing training efforts and increasing the adaptability to new analytical needs. The experiments demonstrate that the LLMs PAPEL utilizes (LLaMA and Chat GPT models) achieve robust performance in privacy policy annotation, with F1 scores reaching 0.8 and above (using the OPP-115 gold standard), underscoring the effectiveness of simpler prompts across various advanced language models.

Related papers

• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• LLM-CI: Assessing Contextual Integrity Norms in Language Models [1.1715858161748576]
  Large language models (LLMs) may inadvertently encode societal preferences and norms. This is especially challenging due to prompt sensitivity$-$small variations in prompts yield different responses. We present LLM-CI, the first open-sourced framework to assess encoded norms.
  arXiv  Detail & Related papers  (2024-09-05T17:50:31Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• Large Language Models: A New Approach for Privacy Policy Analysis at Scale [1.7570777893613145]
  This research proposes the application of Large Language Models (LLMs) as an alternative for effectively and efficiently extracting privacy practices from privacy policies at scale. We leverage well-known LLMs such as ChatGPT and Llama 2, and offer guidance on the optimal design of prompts, parameters, and models. Using several renowned datasets in the domain as a benchmark, our evaluation validates its exceptional performance, achieving an F1 score exceeding 93%.
  arXiv  Detail & Related papers  (2024-05-31T15:12:33Z)
• PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models [1.1704154007740835]
  This work investigates the impact of fine-tuning and data selection on economic and political biases in Large Language Models (LLMs) We introduce PoliTune, a fine-tuning methodology to explore the systematic aspects of aligning LLMs with specific ideologies. We introduce a systematic method for using the open-source Llama3-70B for dataset selection, annotation, and synthesizing a preferences dataset for Direct Preference Optimization (DPO) to align the model with a given political ideology.
  arXiv  Detail & Related papers  (2024-04-10T16:30:09Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models [41.38520841504846]
  Large language models (LLMs) can provide automated preference feedback solely from image inputs to guide decision-making. In this study, we train a multimodal LLM, termed CriticGPT, capable of understanding trajectory videos in robot manipulation tasks. Experimental evaluation of the algorithm's preference accuracy demonstrates its effective generalization ability to new tasks. Performance on Meta-World tasks reveals that CriticGPT's reward model efficiently guides policy learning, surpassing rewards based on state-of-the-art pre-trained representation models.
  arXiv  Detail & Related papers  (2024-02-22T03:14:03Z)
• Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications [0.7832189413179361]
  Large Language Models (LLMs) excel in comprehending and generating human-like text. This paper explores strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems.
  arXiv  Detail & Related papers  (2023-11-21T02:01:01Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.