Source-Grounded Data Generation for Text-to-JSON Learning
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Computer Science > Computation and Language
Title:Source-Grounded Data Generation for Text-to-JSON Learning
Abstract: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%.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.20072 [cs.CL] |
| (or arXiv:2606.20072v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.20072
arXiv-issued DOI via DataCite (pending registration)
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