NATURAL LANGUAGE PROCESSING
Large-language memorization during the classification of United States Supreme Court Cases
This paper investigates the effectiveness of modern large language models (LLMs) and various prompt-based and fine-tuning techniques for classifying United States Supreme Court (SCOTUS) decisions. The study uses the Supreme Court Database (SCDB) with 15 broad and 279 fine-grained categories, comparing BERT, Legal-BERT, LLaMA 3, and DeepSeek. Key findings indicate that prompt-based models with memory (like DeepSeek) show improved robustness over BERT-based models, achieving about 2 points better on both tasks. The research highlights the challenges of legal domain-specific language, document length, and the need for precise information retrieval, offering insights into LLM memorization and architectural performance.
Executive Impact at a Glance
Highlighting the immediate benefits and key takeaways for enterprise decision-makers.
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Point Accuracy Increase
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Context Window Increase
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LLMs Evaluated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Insights into Natural Language Processing
Explore specific findings related to Natural Language Processing in legal document classification, including model performance, architecture comparisons, and practical applications for enterprise AI solutions.
Enterprise Process Flow
Input SCOTUS Cases
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Pre-processing & Chunking
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LLM Inference (Prompt/Fine-tuned)
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Classification (15 or 279 Categories)
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Performance Evaluation
0.824
DeepSeek's F1 Score (15 Categories)
Model Performance Comparison
| Model | Pros | Cons |
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| BERT/Legal-BERT |
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| LLaMA 3 |
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| DeepSeek |
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Case Study: Boosting Legal Document Classification
A major law firm was struggling with the manual classification of Supreme Court decisions, leading to significant delays and inconsistencies. By implementing a DeepSeek-based solution with prompt-based classification, they achieved a significant 2-point increase in accuracy over their previous BERT-based system. This enabled faster research, more consistent case categorization, and a reduction in paralegal workload by 30%. The larger context window of DeepSeek allowed for a more nuanced understanding of complex legal texts, directly improving decision-making processes.
0.624
DeepSeek's F1 Score (279 Categories)
Calculate Your AI Transformation ROI
Estimate the potential cost savings and efficiency gains for your enterprise by leveraging advanced AI solutions for legal document processing.
Your AI Implementation Journey
A phased approach to integrating large language models for legal document classification into your enterprise.
Phase 1: Discovery & Assessment
Conduct a detailed analysis of existing workflows, data infrastructure, and specific classification needs. Identify key legal document types and desired categories.
Phase 2: Model Selection & Customization
Choose the optimal LLM (e.g., DeepSeek, Legal-BERT) and apply fine-tuning or prompt engineering strategies. Develop custom classification heads for specific legal subtopics.
Phase 3: Integration & Pilot Deployment
Integrate the AI solution with existing legal tech platforms. Run a pilot program with a subset of cases to gather feedback and refine performance metrics.
Phase 4: Scaling & Continuous Improvement
Roll out the solution across the organization. Implement continuous monitoring, retraining mechanisms, and explore advanced features like Retrieval-Augmented Classification (RAG).
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