LLM-based Commercial Contract Section Categorization System
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An LLM-based Commercial Contract Section Categorization System is a document section categorization system that is an LLM-based NLP system (to solve a commercial contract section categorization task).
- Context:
- It can (typically) focus on categorizing individual sections of commercial contracts, such as identifying title page, articles, signature page, exhibits.
- It can (typically) employ advanced natural language processing techniques to accurately discern the purpose and content of various contract sections, making it highly useful for legal professionals and contract managers in organizing and understanding contract details.
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- Example(s):
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- Counter-Example(s):
- A Rule-Based Contract Section Categorization System, which depends on predetermined rules and keywords to identify contract sections.
- A General Document Segmentation System, which divides text into segments without understanding the legal significance of those segments.
- LLM-based Commercial Contract Categorization System, which focuses on categorizing entire contracts rather than their individual sections.
- See: Large Language Models, Natural Language Processing, Document Management System, Text Segmentation, LegalTech, Artificial Intelligence in Law.
References
2024
- GPT-4
- PROMPT:
Given the text of a contract section, your task is to determine its category within the contract. The category name should succinctly reflect the nature of the contract section, taking into account the type of content (e.g., definitions, obligations, rights, termination conditions) and its function within the overall contract. Aim for a descriptive yet concise label, typically consisting of 3 to 10 words. Example Categories: Definitions Section Obligations and Rights Casualty Damages and Restoration Article Termination Conditions Signature Page Exhibits and Appendices Here is the text of the contract section to categorize: <CONTRACT_SECTION>PASTE_CONTRACT_SECTION_TEXT_HERE</CONTRACT_SECTION> Now, provide the concise category name: