Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
Researchers have developed a large language model (LLM) framework to improve the extraction and comparability of segment disclosures from financial reports like Form 10-K. This system addresses challenges in completeness and comparability by preserving both reportable and nested segment information. The LLM-based approach can also incorporate data across multiple filings to support longitudinal and cross-firm analysis, demonstrating its potential to enhance financial reporting. AI
IMPACT Enhances financial analysis tools by improving data extraction and comparability from corporate filings.