Reference-based Evaluation

An evaluation approach that assesses model output quality without relying on ground-truth reference answers or golden datasets.

An evaluation method that uses software, benchmarks, metrics, or models to assess the quality, correctness, or performance of AI systems without manual review.

An evaluation approach that uses another trained model to assess the quality, correctness, safety, or other properties of an AI system's outputs.

A curated and validated collection of high-quality reference examples with trusted labels or expected outputs that serves as a benchmark for evaluating, testing, and monitoring the performance of AI models and applications.

A curated collection of test examples, inputs, and expected outcomes used to measure the quality, accuracy, safety, and reliability of AI models and systems.

An evaluation metric that measures whether a model's prediction exactly matches the expected reference answer, without allowing partial credit.