Stanford EDGAR Filings Dataset Transforms U.S. Corporate Disclosures into Efficient Pretraining Data

The Stanford EDGAR Filings Dataset (SEFD) addresses the growing scarcity of clean long-context documents for training large language models (LLMs) by providing an open reconstruction of SEC filings into layout-faithful MultiMarkdown. This dataset includes a wide range of financial documents such as audited financial statements and market-moving event filings, making them accessible for long-context pretraining data, financial reasoning, and compliance. SEFD-v1 consists of 152 billion tokens and features less than 0.1% overlap with Common Crawl-derived datasets. Additionally, two benchmarks, EDGAR-Forecast and EDGAR-OCR, are introduced to evaluate numerical forecasting and transcription of complex financial tables, respectively.