Title: Large Language Model Prompt Chaining for Long Legal Document Classification

Author: Dietrich Trautmann

Published: 2023-08-08

Summary: This paper introduces and evaluates the use of prompt chaining as a method to improve the classification of complex legal documents. By breaking down tasks into smaller parts, this method addresses the challenges posed by the domain-specific language and length of these documents.

Highlights:

• Demonstrates a successful application of prompt chaining that starts with creating summaries and ends with assigning classifications based on exemplar texts.
• Shows that this technique can outperform larger models on micro-F1 scores.
• Uses fewer-shot and in-context learning to achieve these results.
• Highlights its potential to make AI-based legal document processing more accurate and efficient.

Importance: The method proves crucial for enhancing the accuracy and efficiency of AI applications in professional settings like law, saving time, and improving outputs.

Future Research Directions:

• Exploring scalability of prompt chaining in other domains.
• Optimization techniques for better integration of LLMs in professional environments.

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