Evaluation Metrics
Definition
Evaluation metrics are quantitative measures used to assess how well AI models perform specific tasks, including accuracy, precision, recall, F1 score, and domain-specific metrics for NLP and generation quality.
Why It Matters
You can’t improve what you can’t measure. Evaluation metrics tell you whether your AI system actually works, not based on vibes, but on quantifiable performance against specific criteria.
The challenge is choosing the right metrics. A chatbot might have high accuracy on a test set but give terrible user experiences. A summarization model might score well on ROUGE but produce incoherent text. Metrics are proxies for what you care about, and picking the wrong proxy leads to optimizing the wrong thing.
For AI engineers, understanding evaluation metrics is fundamental. You need to select appropriate metrics during development, set baselines, track improvements, and communicate performance to stakeholders. “The model is 87% accurate” means nothing without context. Accurate at what task, measured how, compared to what baseline?
Implementation Basics
Evaluation metrics fall into several categories:
Classification Metrics For tasks with discrete outputs: accuracy (overall correctness), precision (how many positive predictions were correct), recall (how many actual positives were found), and F1 (harmonic mean of precision and recall). Use confusion matrices to understand error patterns.
Generation Metrics For text generation: BLEU and ROUGE measure overlap with reference texts. Perplexity measures how “surprised” a model is by text. These automated metrics correlate imperfectly with human judgment, so use them as directional signals.
Retrieval Metrics For search and RAG: precision@k (how many of the top k results are relevant), recall@k (how many relevant items appear in top k), MRR (mean reciprocal rank of first relevant result), and nDCG (normalized discounted cumulative gain).
Custom Metrics Production systems often need domain-specific metrics. For customer service, measure resolution rate. For code generation, measure test pass rate. For recommendations, measure click-through or conversion.
Human Evaluation Automated metrics don’t capture everything. For generation quality, style, helpfulness, and safety, you need human evaluators. Structure evaluations with clear rubrics and multiple evaluators to reduce subjectivity.
Always measure against a baseline. Improvement metrics like “10% better than previous model” or “matches human performance” provide actionable context.
Source
Evaluation methods for NLP systems encompass multiple dimensions including task performance, robustness, fairness, and efficiency.
https://arxiv.org/abs/2006.14799