LegiLM: A Fine-Tuned Legal Language Model for Data Compliance

• URL: http://arxiv.org/abs/2409.13721v1
• Date: Mon, 9 Sep 2024 02:06:52 GMT
• Title: LegiLM: A Fine-Tuned Legal Language Model for Data Compliance
• Authors: Linkai Zhu, Lu Yang, Chaofan Li, Shanwen Hu, Lu Liu, Bin Yin,
• Abstract summary: LegiLM is a novel legal language model specifically tailored for consulting on data or information compliance. It has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications.
• Score: 5.256747140296861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise. This paper introduces LegiLM, a novel legal language model specifically tailored for consulting on data or information compliance. LegiLM leverages a pre-trained GDPR Fines dataset and has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. By incorporating a specialized dataset that includes global data protection laws, meticulously annotated policy documents, and relevant privacy policies, LegiLM is optimized for addressing data compliance challenges. The model integrates advanced legal reasoning methods and information retrieval enhancements to enhance accuracy and reliability in practical legal consulting scenarios. Our evaluation using a custom benchmark dataset demonstrates that LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications, setting a new benchmark for AI-driven legal compliance solutions. Our resources are publicly available at https://github.com/DAOLegalAI/LegiLM

Related papers

• How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review [15.15468770348023]
  We evaluate large language models' performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) Through an empirical assessment, we investigate the capacity of several prominent LLMs, including BERT, GPT-3.5, GPT-4, and custom models, in executing privacy compliance checks and technical privacy reviews. While LLMs show promise in automating privacy reviews and identifying regulatory discrepancies, significant gaps persist in their ability to fully comply with evolving legal standards.
  arXiv  Detail & Related papers  (2024-09-04T01:51:37Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• The Data Minimization Principle in Machine Learning [61.17813282782266]
  Data minimization aims to reduce the amount of data collected, processed or retained. It has been endorsed by various global data protection regulations. However, its practical implementation remains a challenge due to the lack of a rigorous formulation.
  arXiv  Detail & Related papers  (2024-05-29T19:40:27Z)
• PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification [0.0]
  We propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. PrivComp-KG is designed to efficiently store and retrieve comprehensive information concerning privacy policies. It can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations.
  arXiv  Detail & Related papers  (2024-04-30T17:44:44Z)
• Enhancing Legal Compliance and Regulation Analysis with Large Language Models [0.0]
  This research explores the application of Large Language Models (LLMs) to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time financial constraints.
  arXiv  Detail & Related papers  (2024-04-26T16:40:49Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice [62.44110411199835]
  We build on literature in machine learning law to propose framework for limiting collection based on data interpretation that ties data to system performance. We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique.
  arXiv  Detail & Related papers  (2021-07-16T19:59:01Z)
• Detecting Compliance of Privacy Policies with Data Protection Laws [0.0]
  Privacy policies are often written in extensive legal jargon that is difficult to understand. We aim to bridge that gap by providing a framework that analyzes privacy policies in light of various data protection laws. By using such a tool, users would be better equipped to understand how their personal data is managed.
  arXiv  Detail & Related papers  (2021-02-21T09:15:15Z)
• The SPECIAL-K Personal Data Processing Transparency and Compliance Platform [0.1385411134620987]
  SPECIAL EU H 2020 project can be used to represent data policies and data and events sharing. System can verify that data processing and sharing complies with the data subjects consent.
  arXiv  Detail & Related papers  (2020-01-26T14:30:09Z)

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.