People, rules, existing models, or measurement systems produce labels under a written guideline. Quality control uses calibration tasks, overlap, adjudication, and audits to identify ambiguity and error.
Data labeling assigns target values, categories, spans, preferences, or other annotations to examples.
People, rules, existing models, or measurement systems produce labels under a written guideline. Quality control uses calibration tasks, overlap, adjudication, and audits to identify ambiguity and error.