DECODEM: Benchmark for Automated Extraction of Corporate Governance Variables from Legal Documents
A new preprint introduces DECODEM, a benchmark dataset designed to evaluate automated extraction of corporate governance variables from organizational documents such as charters and bylaws. The authors use high-quality human annotations to assess the performance of various large language model (LLM) pipelines on document-level binary classification tasks. Results indicate that automated extraction is feasible and achieves high accuracy for many governance provisions, though performance varies across variables. The study also finds that pipeline design can partly compensate for differences in model capability.
Why it matters: This work provides a standardized benchmark and systematic evaluation, showing that current LLMs can accurately extract legally relevant information from complex corporate documents, potentially reducing the need for manual coding in legal research.
Full story at: arXiv Computation and Language ↗