Reproduce “Metadata Extraction Leveraging Large Language Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Cuize Han, Sesh Jalagam
Researchers from Box AI Platform developed an optimized framework for metadata extraction from legal documents, leveraging existing Large Language Models (LLMs) through a multi-stage pipeline. The system achieved improved F1 scores on contract datasets, reduced operational costs, and provided a practical, scalable solution for enterprise environments.
Key insights
- Optimal text conversion is foundational, as its quality directly impacts downstream extraction accuracy and efficiency.
- Strategic chunk selection significantly reduces LLM token usage and costs while boosting extraction quality, especially in scenarios with limited context windows.
- Combining Chain of Thought prompting with structured output through tool calling effectively improves LLM performance and reliability for complex fields, though effectiveness is model-dependent.
- An LLM can effectively act as a 'judge' for post-processing, validating and correcting initial extraction outputs and enabling privacy-preserving system monitoring.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.