Evaluation Methodology

Summary

A recap of why foundation-model evaluation is hard and how language-modeling metrics, exact methods, AI as a judge, and comparative ranking fit together.

Chapter Summary

The stronger AI models become, the higher the potential for catastrophic failures, which makes evaluation even more important. At the same time, evaluating open-ended, powerful models is challenging. These challenges make many teams turn toward human evaluation.

Having humans in the loop for sanity checks is always helpful, and in many cases, human evaluation is essential. However, this chapter focused on different approaches to automatic evaluation.

What This Chapter Covered

Why Evaluation Is Harder

This chapter starts with a discussion on why foundation models are harder to evaluate than traditional ML models. While many new evaluation techniques are being developed, investments in evaluation still lag behind investments in model and application development.

Language Modeling Metrics

Since many foundation models have a language model component, we zoomed into language modeling metrics, including perplexity and cross entropy. Many people I've talked to find these metrics confusing, so I included a section on how to interpret these metrics and leverage them in evaluation and data processing.

Exact Evaluation

This chapter then shifted the focus to the different approaches to evaluate open-ended responses, including functional correctness, similarity scores, and AI as a judge. The first two evaluation approaches are exact, while AI as a judge evaluation is subjective.

AI as a Judge

Unlike exact evaluation, subjective metrics are highly dependent on the judge. Their scores need to be interpreted in the context of what judges are being used. Scores aimed to measure the same quality by different AI judges might not be comparable.

AI judges, like all AI applications, should be iterated upon, meaning their judgments change. This makes them unreliable as benchmarks to track an application's changes over time. While promising, AI judges should be supplemented with exact evaluation, human evaluation, or both.

Ranking Models

When evaluating models, you can evaluate each model independently, and then rank them by their scores. Alternatively, you can rank them using comparative signals: which of the two models is better?

Comparative evaluation is common in sports, especially chess, and is gaining traction in AI evaluation. Both comparative evaluation and the post-training alignment process need preference signals, which are expensive to collect. This motivated the development of preference models: specialized AI judges that predict which response users prefer.

What Comes Next

While language modeling metrics and hand-designed similarity measurements have existed for some time, AI as a judge and comparative evaluation have only gained adoption with the emergence of foundation models. Many teams are figuring out how to incorporate them into their evaluation pipelines.

Figuring out how to build a reliable evaluation pipeline to evaluate open-ended applications is the topic of the next chapter.

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