Summary of Legalpro-bert: Classification Of Legal Provisions by Fine-tuning Bert Large Language Model, By Amit Tewari
LegalPro-BERT: Classification of Legal Provisions by fine-tuning BERT Large Language Model
by Amit Tewari
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to contract analysis using artificial intelligence (AI) and natural language processing (NLP). The authors highlight the complexity of classifying legal provisions in contracts, which requires domain-specialized legal language for model training. To address this issue, they introduce LegalPro-BERT, a BERT transformer architecture model fine-tuned for efficient classification of legal provisions. The model outperforms previous benchmark results and has implications for automating contract review processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Contract analysis is an important process in organizations to avoid business risk and liability. Current approaches require trained lawyers or paralegals to identify and classify key provisions, which can be time-consuming and costly. This paper introduces a new AI-powered approach to simplify this task. The authors use a pre-trained language model, BERT, and fine-tune it for legal taxonomy. They show that their LegalPro-BERT model outperforms previous benchmarks, making contract analysis more efficient and accessible. |
Keywords
» Artificial intelligence » Bert » Classification » Language model » Natural language processing » Nlp » Transformer
