用表格验证生成文本到JSON数据，提升结构化信息提取效果
Source-Grounded Data Generation for Text-to-JSON Learning
- 基于表格构建文本与JSON的合成数据，确保生成值真实可靠
- 在851条测试样本上，准确率从31%提升至74%，值匹配率达90.7%
- 适合需要高质量标注数据的金融、医疗等领域的文本结构化任务
从财务文件到临床记录，传统行业依赖长篇非结构化文档存储高价值信息。将其可靠地转换为机器可读的结构化表示是自动化系统利用这些内容的前提。JSON是此类结构化提取的理想目标，但构建可靠且可扩展的文本到JSON训练数据仍具挑战。为此，我们提出STAGE（Spreadsheet-grounded Text-to-JSON Artifact GEneration），一种基于源数据（表格）的生成管道，利用大语言模型实现规模化合成，同时通过底层表格验证生成的真值。在包含851个样本的STAGE-Eval基准测试中，STAGE生成的数据优于现有方法，使Qwen3-4B的精确匹配率从31.37%提升至74.27%，值准确率从45.46%提升至90.69%。
原文摘要 · Abstract (English)
From financial filings to clinical records, legacy industries rely heavily on long, unstructured documents to store high-value information. Reliably extracting this information into structured, machine-readable representations is a key prerequisite to making the contents accessible to automated systems. JSON is a natural target for such structured extraction, yet constructing reliable and scalable text-to-JSON training data remains challenging. To address this gap, we propose STAGE (Spreadsheet-grounded Text-to-JSON Artifact GEneration), a source-grounded data generation pipeline that constructs reports and JSON schema by using LLMs for scalable synthesis while validating ground-truth values against the underlying spreadsheet. Evaluations on STAGE-Eval, our source-grounded benchmark with an 851-example test set, show that STAGE produces stronger training data than existing approaches. This improves Qwen3-4B exact match from 31.37% to 74.27% and value accuracy from 45.46% to 90.69%.
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