Beginner · Evaluation

Evaluation Metrics

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Quantitative measures used to assess the performance, accuracy, and quality of AI models for specific tasks.

Technical Definition

Quantitative measures used to assess the performance, accuracy, and quality of AI models for specific tasks.

How it works

Evaluation metrics are critical for understanding how well an AI model is performing and for comparing different models. For LLMs, metrics go beyond traditional accuracy, including measures like ROUGE for summarization, BLEU for translation, or human preference scores for overall quality. Choosing appropriate metrics is essential for effective model development and deployment.

Related Concepts

• Human-in-the-loop — System designs where humans review, correct, or guide AI outputs as part of the workflow.
• BLEU (Bilingual Evaluation Understudy) — A metric to evaluate machine translation quality by comparing generated text to human references based on N-gram overlap.