Large Language Model Prompt Chaining for Long Legal Document Classification - The paper discusses a novel approach to classifying extensive legal documents using prompt chaining with large language models. It simplifies complex classification tasks by breaking them into smaller, more manageable parts, starting with summarization and culminating in accurate label assignment.

Key Points:

• Strategic prompt chaining decomposes the classification task for intricate legal documents.
• The process includes summarization, semantic search, and few-shot prompt in-context learning.
• Results indicate this method outperforms zero-shot capabilities of even larger models like ChatGPT.
• The study provides a practical solution for improving legal document classification without the need for larger models.

The implications for legal tech and AI are significant, as this methodology can lead to more efficient and cost-effective legal document processing. The study not only offers a glimpse into the practical advancements in LLM applications but also illustrates the scalability of smaller models when properly guided through prompt chaining techniques. Read more.