Fine-Tuning Legal AI Models: Contract Review and Legal Research

• An understanding of natural language processing fundamentals
• Experience with Python and machine learning libraries such as Hugging Face Transformers
• Familiarity with legal texts and basic legal document structures

Audience

• Legal tech engineers
• AI developers for law firms
• Machine learning professionals working with legal data

Fine-tuning is the process of adapting pre-trained NLP models to specialized domains such as law and legal documentation.

This instructor-led, live training (online or onsite) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in legal service environments.

By the end of this training, participants will be able to:

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to improve model accuracy on legal tasks.
• Deploy models to assist with contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Legal AI and Fine-Tuning

• Overview of legal tech and its evolution
• Applications of NLP in law: contracts, case law, compliance
• Benefits and limitations of using pre-trained models in legal domains

Preparing Legal Data for Fine-Tuning

• Types of legal documents: contracts, terms, case law, statutes
• Text cleaning, segmentation, and clause extraction
• Annotating legal data for supervised learning

Fine-Tuning NLP Models for Legal Tasks

• Choosing a pre-trained model: BERT, LegalBERT, RoBERTa, etc.
• Setting up a fine-tuning pipeline with Hugging Face
• Training on legal classification and extraction tasks

Contract Review Automation

• Detecting clause types and obligations
• Highlighting risk terms and compliance issues
• Summarizing long contracts for quick review

Legal Research Assistance with AI

• Information retrieval and ranking for case law
• Question answering on statutes and regulations
• Building a legal document chatbot or assistant

Evaluation and Interpretability

• Metrics: F1, precision, recall, accuracy
• Model explainability in high-stakes legal contexts
• Tools for clause-level confidence scoring and auditing

Deployment and Integration

• Embedding models in legal research platforms or review tools
• APIs and interface considerations for law firm use
• Maintaining privacy, version control, and update workflows

Summary and Next Steps