LegalPro-BERT: Classification of Legal Provisions by fine-tuning BERT Large Language Model

TL;DR

LegalPro-BERT is a fine-tuned BERT model designed specifically for classifying legal provisions in contracts, addressing the challenges of legal language and limited labeled data, and outperforming previous benchmarks.

Contribution

The paper introduces LegalPro-BERT, a domain-specific BERT model fine-tuned for legal clause classification, improving accuracy over existing models.

Findings

LegalPro-BERT outperforms previous benchmarks in legal clause classification.

Fine-tuning BERT on legal data enhances understanding of legal vocabulary.

The approach reduces the need for extensive legal expert annotation.

Abstract

A contract is a type of legal document commonly used in organizations. Contract review is an integral and repetitive process to avoid business risk and liability. Contract analysis requires the identification and classification of key provisions and paragraphs within an agreement. Identification and validation of contract clauses can be a time-consuming and challenging task demanding the services of trained and expensive lawyers, paralegals or other legal assistants. Classification of legal provisions in contracts using artificial intelligence and natural language processing is complex due to the requirement of domain-specialized legal language for model training and the scarcity of sufficient labeled data in the legal domain. Using general-purpose models is not effective in this context due to the use of specialized legal vocabulary in contracts which may not be recognized by a general model. To address this problem, we propose the use of a pre-trained large language model which is subsequently calibrated on legal taxonomy. We propose LegalPro-BERT, a BERT transformer architecture model that we fine-tune to efficiently handle classification task for legal provisions. We conducted experiments to measure and compare metrics with current benchmark results. We found that LegalPro-BERT outperforms the previous benchmark used for comparison in this research.