Preference Data
Data that records preferences between AI outputs or behaviors, typically used to train or optimize models toward preferred responses.
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Related terms
Information, evaluations, corrections, or preferences provided by human users or reviewers about an AI system's outputs or behavior, which can be used to improve model performance, refine prompts, optimize workflows, or guide future decisions.
Preference OptimizationThe process of optimizing an AI system to produce outputs that better match preferred responses, behaviors, or outcomes.
Feedback CollectionThe process of gathering feedback from users, human evaluators, or automated systems to assess AI system performance and provide data for evaluation, improvement, and model refinement.
Feedback AggregationThe process of collecting, combining, and organizing feedback from multiple users, evaluators, or automated systems to identify trends, measure performance, and guide AI system improvements.
Reward ModelingThe process of training a mathematical model to score AI outputs based on human or automated preferences.