Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction

Priyashree Roy; Sujitha Martin; Mohammad Rostami; Spencer Romo; Renhao Xue; Bob Strahan; Diego A. Socolinsky; Boyi Xie; Md Mofijul Islam

arXiv · 2608.01792

Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction

Priyashree Roy·Sujitha Martin·Mohammad Rostami·Spencer Romo·Renhao Xue·Bob Strahan·Diego A. Socolinsky·Boyi Xie·Md Mofijul Islam

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Abstract

Intelligent document processing (IDP) with vision-language models (VLMs) hinges on confidence scores trustworthy enough to route extractions between automation and human review. Existing document benchmarks are dominated by clean, high-quality samples, leaving low accuracy regions too sparse for calibration assessment. We introduce ConfBench, the first calibration-specific benchmark for key information extraction (KIE), built by applying 20 controlled degradation pipelines to a diverse document set, yielding 1,346 variants and 70K+ entity-level evaluations spanning the full accuracy spectrum. We evaluate four proprietary and three open-weight VLMs under verbalized and log-probability confidence estimation methods across three input modalities, and find: (i) OCR+Image modality results in more accurate confidence estimates; (ii) model capability is the dominant factor: within the Claude family confidence quality scales monotonically with capability, while across families parameter count is a poor predictor; (iii) calibration quality varies widely across models, from near-perfect to severely overconfident, and per-model post-hoc correction rescales these absolute confidence values for threshold-based routing without altering ranking-based operational metrics; and (iv) log-probability with first-token aggregation consistently outperforms mean-token and margin aggregations. We also introduce ECARB, a review-budget metric translating discriminative gains into operational savings. We release ConfBench publicly to enable systematic study of confidence estimators and calibration methods for trustworthy IDP application deployment.

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BibTeX RIS

Priyashree Roy, Sujitha Martin, Mohammad Rostami, Spencer Romo, Renhao Xue, Bob Strahan, Diego A. Socolinsky, Boyi Xie, Md Mofijul Islam. 2026-08-03. Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction. https://arxiv.org/abs/2608.01792

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