LegiLM: A Fine-Tuned Legal Language Model for Data Compliance
This addresses the complex task of data compliance for legal professionals and organizations, representing an incremental improvement by fine-tuning on specialized legal data.
The paper tackles the problem of ensuring compliance with international data protection standards by introducing LegiLM, a fine-tuned legal language model that automatically assesses breaches in data security and privacy regulations, demonstrating excellence in detecting breaches and providing legal justifications on a custom benchmark dataset.
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