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Evaluating large language models (LLMs) is crucial to ensure they deliver accurate, safe, and useful outputs. After all, without rigorous assessment, models may generate incorrect, biased, or harmful content that undermines trust and viability. Evaluation helps developers understand a model’s strengths and weaknesses, guide improvements, and ensure alignment with practical needs.

How do we evaluate LLMs?

There are two primary methods for evaluating LLMs: automatic metrics and human evaluation. Automatic metrics use algorithms to score outputs objectively, enabling rapid assessment at large scale. Human evaluation, on the other hand, involves subjective judgment by people who assess fluency, relevance, and appropriateness more holistically, capturing nuances that machines may miss.

When to Use Which Approach

The choice between automatic and human evaluation depends on factors like scale, cost, and task type. Automatic metrics are ideal for frequent, large-scale testing where efficiency is critical, but their insights are limited. Human evaluation is best for nuanced tasks, high-stakes applications, or final validation, despite being slower and more costly. Often, a blend of both provides the most reliable and actionable insights.