Confidence Scores

Confidence scores are numerical estimates that indicate how reliable or accurate a machine-generated translation is likely to be. Translation systems generate these scores to help determine whether a translation can be used as is or should be reviewed by a human translator.

Confidence scores are often used alongside machine translation quality estimation to evaluate translation output without requiring a reference translation.

How Confidence Scores Work

Confidence scores are generated using machine learning models that analyze translation output.

Translation Probability Analysis AI models evaluate how likely a translation is based on patterns learned during training.

Quality Prediction The system estimates how accurate or natural the translation output is likely to be.

Segment-Level Scoring Confidence scores may be assigned to individual segments or entire documents.

Workflow Decisions Low-confidence segments can be flagged for human review, while high-confidence segments may move forward automatically.

Benefits of Confidence Scores

Confidence scoring helps organizations optimize translation workflows.

• Identifies potentially inaccurate translations
• Prioritizes human review where needed
• Reduces manual review workload
• Improves translation workflow efficiency
• Supports scalable multilingual content production

Confidence Scores in Localization Workflows

Modern AI translation platforms often use confidence scores to help teams manage quality at scale. By predicting translation reliability, these systems allow organizations to automate parts of the localization process while maintaining quality standards.

LILT’s AI-powered translation platform uses advanced models and feedback loops to evaluate translation output and help teams identify which segments may require additional review.