ExtractConf fuses Hunter-Mapper disagreement with LLM uncertainty, OCR, image quality and layout into a classifier that reaches 0.928 ROC AUC on DocILE invoices and 0.858 on CORD receipts, cutting selective prediction risk 70% versus logprob baselines.
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Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction
ExtractConf fuses Hunter-Mapper disagreement with LLM uncertainty, OCR, image quality and layout into a classifier that reaches 0.928 ROC AUC on DocILE invoices and 0.858 on CORD receipts, cutting selective prediction risk 70% versus logprob baselines.