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New benchmark ExtractBench evaluates enterprise document extraction agents

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ExtractBench, a new benchmark designed to evaluate schema-guided document extraction capabilities in enterprise settings. This benchmark assesses agents on value accuracy, record completeness, grounding, and cost, utilizing a dataset of 4,869 pages across 370 enterprise documents. Initial results show that while commercial VLMs struggle with long documents, coding agents are accurate but expensive. LlamaExtract Agentic Plus emerged as the top performer, offering comparable accuracy to coding agents at a significantly lower cost. AI

IMPACT This benchmark could drive improvements in enterprise AI agents for accurate and cost-effective data extraction.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for AI capabilities.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark ExtractBench evaluates enterprise document extraction agents

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo ·

    ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction

    arXiv:2607.29677v1 Announce Type: new Abstract: 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 groundin…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction

    Enterprise workflows increasingly rely on agents for 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 sc…

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