Confidence Threshold

Confidence thresholds should route low-quality extraction results to human review, rather than sending them directly into downstream automation.

In production, confidence thresholds are usually set separately by field type and source: higher confidence is required for amounts, dates, and compliance fields, while it can be relaxed for ordinary notes.

A confidence threshold is a lightweight uncertainty measure, implemented by checking whether the output length is far below the expected value, detecting vague wording, or comparing the consistency of multiple sampled results.

When deploying decision models in production, fallback rules, confidence thresholds, and log tracing are usually introduced to balance benefits and controllability.

A common engineering practice is to set different confidence thresholds for different field types: high-risk fields such as amounts and account numbers are given high thresholds, while low-risk fields such as notes and addresses are given low thresholds.

Confidence thresholds should be adjusted dynamically based on the online manual correction rate—if the manual rejection rate for a certain type of field remains high, it indicates the threshold is too loose and needs to be tightened.

The confidence threshold is a double-edged sword: lowering the threshold increases recall but decreases precision; raising the threshold increases precision but increases missed detections.

HITL grading strategies commonly used in industry at present include: whitelists/blacklists based on action type, labels based on resource sensitivity, and dynamic triggering based on confidence thresholds.

For scenarios with low fault tolerance (such as finance and healthcare), the threshold should be set higher, while for scenarios with high fault tolerance (such as content pre-screening and tag classification), it can be appropriately relaxed.

An excessively high threshold (such as 95%) will cause a large number of tasks to be handed over to humans, reducing automation gains, while an excessively low threshold (such as 50%) will allow low-quality results to enter the production environment.

In a confidence-based routing mechanism, the decision-maker revolves around judging whether a certain result 'is appropriate' and sets a confidence threshold as a binary branch.

The study introduces a confidence threshold to dynamically adjust the quality–latency trade-off at inference time, providing the widest and consistently competitive operating range.

Setting a higher confidence threshold reduces the automation rate but increases the accuracy of the portion that is automatically processed; setting a lower threshold increases automation coverage but raises the number of extraction errors.

Confidence score thresholds should be calibrated using annotated data for specific business scenarios, rather than directly applying empirical values such as 0.7 or 0.8.