Over-refusal is the failure mode where models decline too many benign or allowed requests due to overly conservative safety behavior - excessive refusal reduces assistant usefulness and user trust.

What Is Over-refusal?

• Definition: Elevated refusal rate on non-violating prompts that should receive normal assistance.
• Typical Causes: Aggressive safety thresholds, weak context interpretation, or over-generalized refusal training.
• Observed Symptoms: Benign technical queries incorrectly treated as harmful requests.
• Measurement Focus: Benign-refusal error rate across domains and user cohorts.

Why Over-refusal Matters

• Utility Loss: Users cannot complete legitimate tasks reliably.
• Experience Degradation: Repeated unwarranted refusal feels frustrating and arbitrary.
• Adoption Risk: Overly restrictive systems lose credibility in professional workflows.
• Fairness Concern: Some linguistic styles may be disproportionately over-blocked.
• Optimization Signal: Indicates refusal calibration is misaligned with policy intent.

How It Is Used in Practice

• Error Taxonomy: Label over-refusal cases by cause to guide targeted remediation.
• Calibration Tuning: Adjust thresholds and policies by category rather than globally.
• Data Augmentation: Train on benign look-alike prompts to improve disambiguation.

Over-refusal is a critical quality risk in safety-aligned assistants - reducing unnecessary denials is required to maintain practical usefulness while preserving strong harm protections.