斯坦福发布金融SEC文件数据集，优化LLM训练

This was long needed for AI in finance.

Making SEC filings readable for machines without flattening the accounting logic.

Stanford researcher has just released a dataset and methods for a cleaner way to turn SEC filings into useful LLM training data without losing the meaning inside financial tables.

A 152B-token public snapshot and estimate the full archive could become about 550B tokens of long financial documents.

Has less than 0.1% overlap with Common Crawl-derived corpora.

The authors propose SEFD, a rebuilt version of EDGAR filings that keeps table structure, indentation, and financial meaning while using fewer tokens for LLM training.

The dataset turns EDGAR into layout-faithful MultiMarkdown, preserving merged headers, indentation, signs, spans, and table hierarchy while shrinking enormous presentation scaffolding into usable tokens.

Link – arxiv. org/abs/2606.18192v1