Valid Per-Field Selective Risk Control for Document Extraction: Three Failure Modes, a Validity Ladder, and When Conditioning Pays
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Abstract (arXiv)
Per-field accept/review with selective risk at most alpha -- accept a field only if the error rate among accepted fields is controlled -- is the trust contract document-extraction systems need, and the natural procedure silently violates it on real documents. On 13,859 genuine claude-sonnet-5 fields from 800 CORD receipts (49.0% correct) we diagnose three failure modes: document clustering (design effect 1.84-2.45), score-refit leakage (coverage 0.416 at risk 0.127, violating alpha=0.10 in 95% of splits), and a tie-mass pathology (a degenerate score collapses the threshold grid, 0.030 to 0.001). We organize the fixes as a validity ladder, guarantee form stated per tier. A fit/val split protocol restores expected-selective-risk control for a learned fusion: coverage 0.318 at risk 0.096 at nominal alpha=0.10, no tolerance band (production variant 0.326) -- an on-average point whose realized risk exceeds alpha in 47.5% of resplits, not a certificate. Mondrian Learn-then-Test with exact binomial tails yields per-group PAC certificates: field-iid 0.171 at risk 0.068, cluster-corrected 0.140, doc-iid 0.060 -- the only tier matching documents, honestly near-vacuous today. Support-bin, the pre-specified provenance taxonomy, wins every rigor tier on the sonnet CORD capture (p<1e-4, Bonferroni-corrected) -- a win that does not replicate on the same documents under haiku or qwen -- while on higher-accuracy corpora pooled thresholds win: conditioning helps exactly where pooled cannot certify, subsumed by a learned score elsewhere. A frozen-configuration confirmation on selection-untouched claude-haiku-4-5 held at both risk levels, and a blind three-annotator human-gold audit verifies the practical tier's accepted-set risk at 1.3% against its 10% budget (Fleiss' kappa=0.83; labels err one-sidedly pessimistic). Released Apache-2.0 with seed-pinned, regression-gated procedures.
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References · 16
- 2022Conformal Risk Controlcited by 290
- 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
- 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
- 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 254
- 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
- 2023Conformal Prediction With Conditional Guaranteescited by 146
- 2024Language Models with Conformal Factuality Guaranteescited by 128
- 2023Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthinesscited by 121
- 2018Bias-Reduced Uncertainty Estimation for Deep Neural Classifierscited by 106
- 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
- 2024Overcoming Common Flaws in the Evaluation of Selective Classification Systemscited by 31
- 2024VISA: Retrieval Augmented Generation with Visual Source Attributioncited by 28
4 more
- 2025BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotationscited by 8
- 2026ExtractBench: A Benchmark and Evaluation Methodology for Complex Structured Extractioncited by 8
- 2026Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extractioncited by 3
- 2026Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extractioncited by 2
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