ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata. We present ExtractBench, a benchmark for schema-guided extraction and, to our knowledge, the first to score value accuracy, record completeness at scale, grounding, and measured cost together. The evaluation system contains 4,869 pages across 370 enterprise documents, 8 business domains, and 67 document types, with clear tags differentiating their challenge scenarios. The scalable schema and ground-truth curation pipeline combines independent-system...
| Authors | Boyang Zhang, Adrian Lyjak, Eli Stewart et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 5 August 2026 |
Models and datasets referencing this paper
- llamaindex/ExtractBenchDataset
- Nikhil231/ExtractBenchDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.