# 斯坦福发布SEFD：152B token结构化SEC文件数据集

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-17 19:07
- AIHOT 分数：55
- AIHOT 链接：https://aihot.virxact.com/items/cmqhz2jog043qslf007psv6sl
- 原文链接：https://x.com/rohanpaul_ai/status/2067202436943749396

## AI 摘要

斯坦福研究者发布SEFD数据集与处理方法，将SEC EDGAR申报文件转化为适合LLM训练的结构化数据，保留表格结构、缩进、合并表头、符号、跨度及层级关系。公开快照包含152B token，完整档案约550B token。该数据与Common Crawl语料重叠度低于0.1%。采用布局保真的MultiMarkdown格式，大幅压缩原有演示框架，保留财务含义的同时减少token浪费。

## 正文

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.

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Link - arxiv. org/abs/2606.18192v1