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Automatic evaluation

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TL;DR. Automatic evaluation uses software to assess model output quality, often by comparing it to a known correct answer.

Technical Definition

Automatic evaluation uses software to assess model output quality, often by comparing it to a known correct answer.

How it works

Automatic evaluation involves using algorithms or software to quantitatively assess the quality of a machine learning model's output. For tasks with predefined correct answers, this can involve direct comparison; for more complex outputs or generative tasks, specific metrics or even other AI models (autoraters) may be employed.

Related Concepts

  • BLEU (Bilingual Evaluation Understudy) — A metric to evaluate machine translation quality by comparing generated text to human references based on N-gram overlap.
  • Human evaluation — Assessing model performance by using human judgment to rate its outputs.
  • Evaluation Metrics — Quantitative measures used to assess the performance, accuracy, and quality of AI models for specific tasks.

Further Reading

  • Google ML Glossary