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Metadata Extraction Leveraging Large Language Models

Cuize Han, Sesh Jalagam

TL;DR

This paper addresses automated metadata extraction from contracts using an LLM-driven pipeline that integrates robust text conversion, strategic chunk selection, and advanced LLM prompting and tool-based structuring. It evaluates this end-to-end framework on the CUAD dataset and an internal lease corpus, demonstrating substantial gains in extraction accuracy and efficiency, notably via NER-enhanced chunk selection and model-based reranking, achieving higher F1 scores than baselines. An LLM-based grading system further improves accuracy through label correction and enables privacy-preserving online monitoring, reporting an 80.5% match to ground truth versus 73.3% for the base extractor. The work demonstrates practical potential for scalable, cost-conscious contract review and outlines directions for improving uncertainty handling, field-specific ranking, and OCR quality to further broaden adoption.

Abstract

The advent of Large Language Models has revolutionized tasks across domains, including the automation of legal document analysis, a critical component of modern contract management systems. This paper presents a comprehensive implementation of LLM-enhanced metadata extraction for contract review, focusing on the automatic detection and annotation of salient legal clauses. Leveraging both the publicly available Contract Understanding Atticus Dataset (CUAD) and proprietary contract datasets, our work demonstrates the integration of advanced LLM methodologies with practical applications. We identify three pivotal elements for optimizing metadata extraction: robust text conversion, strategic chunk selection, and advanced LLM-specific techniques, including Chain of Thought (CoT) prompting and structured tool calling. The results from our experiments highlight the substantial improvements in clause identification accuracy and efficiency. Our approach shows promise in reducing the time and cost associated with contract review while maintaining high accuracy in legal clause identification. The results suggest that carefully optimized LLM systems could serve as valuable tools for legal professionals, potentially increasing access to efficient contract review services for organizations of all sizes.

Metadata Extraction Leveraging Large Language Models

TL;DR

This paper addresses automated metadata extraction from contracts using an LLM-driven pipeline that integrates robust text conversion, strategic chunk selection, and advanced LLM prompting and tool-based structuring. It evaluates this end-to-end framework on the CUAD dataset and an internal lease corpus, demonstrating substantial gains in extraction accuracy and efficiency, notably via NER-enhanced chunk selection and model-based reranking, achieving higher F1 scores than baselines. An LLM-based grading system further improves accuracy through label correction and enables privacy-preserving online monitoring, reporting an 80.5% match to ground truth versus 73.3% for the base extractor. The work demonstrates practical potential for scalable, cost-conscious contract review and outlines directions for improving uncertainty handling, field-specific ranking, and OCR quality to further broaden adoption.

Abstract

The advent of Large Language Models has revolutionized tasks across domains, including the automation of legal document analysis, a critical component of modern contract management systems. This paper presents a comprehensive implementation of LLM-enhanced metadata extraction for contract review, focusing on the automatic detection and annotation of salient legal clauses. Leveraging both the publicly available Contract Understanding Atticus Dataset (CUAD) and proprietary contract datasets, our work demonstrates the integration of advanced LLM methodologies with practical applications. We identify three pivotal elements for optimizing metadata extraction: robust text conversion, strategic chunk selection, and advanced LLM-specific techniques, including Chain of Thought (CoT) prompting and structured tool calling. The results from our experiments highlight the substantial improvements in clause identification accuracy and efficiency. Our approach shows promise in reducing the time and cost associated with contract review while maintaining high accuracy in legal clause identification. The results suggest that carefully optimized LLM systems could serve as valuable tools for legal professionals, potentially increasing access to efficient contract review services for organizations of all sizes.
Paper Structure (23 sections, 5 figures, 5 tables)

This paper contains 23 sections, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Metadata Extraction
  3. Large Language Models
  4. Related Works
  5. Metadata Extraction Workflow
  6. Text conversion and OCR
  7. Chunk Selection
  8. NER and Borda Re-ranking
  9. Model-based Chunk Re-ranking
  10. LLM-based Information Synthesis
  11. Chain of Thought Prompting
  12. Structured Output through Tool Calling
  13. Experiments
  14. Datasets
  15. OCR and Text Conversion
  16. ...and 8 more sections

Figures (5)

  • Figure 1: Simplified workflow of metadata extraction
  • Figure 2: Neural network re-ranking model
  • Figure 3: Count of extracted fields
  • Figure 4: CoT experiment on the internal contract dataset
  • Figure 5: Online monitoring based on LLM grader