Chapter 10: Evaluation & Benchmarks
Learn how to measure LLM quality rigorously. Master automated metrics like perplexity, BLEU, and ROUGE for specific tasks, understand when human evaluation is necessary, navigate the landscape of benchmark suites, and use red teaming to find failure modes before your users do.
Chapter Overview
Evaluating language models is fundamentally harder than evaluating classifiers or regressors. There is no single "accuracy" metric for open-ended text generation. A model's response to "Explain quantum computing" could be correct in countless different ways, and automated metrics can only capture limited aspects of quality.
The evaluation landscape spans a spectrum from fully automated to fully human. Automated metrics like perplexity measure how well a model predicts text. Task-specific metrics like BLEU and ROUGE compare generated text to reference outputs. LLM-as-judge uses a stronger model to evaluate a weaker one. Human evaluation captures nuances that no automated metric can.
Benchmarks provide standardized tests across capabilities: reasoning (GSM8K), knowledge (MMLU), coding (HumanEval), safety (TruthfulQA), and more. But benchmarks have limitations -- models can be trained on benchmark data, and high benchmark scores don't always translate to real-world usefulness.
Red teaming is adversarial evaluation: deliberately trying to make the model fail, produce harmful content, leak information, or behave unexpectedly. This is essential before deployment because users will inevitably push models beyond their intended use cases.
This chapter covers:
- Perplexity: The fundamental metric for language model quality
- BLEU & ROUGE: Automated metrics for translation, summarization, and text generation
- Human Evaluation: When and how to use human judges effectively
- Benchmark Suites: Standardized tests across reasoning, knowledge, coding, and safety
- Red Teaming: Adversarial testing to find failure modes and safety vulnerabilities
Chapter Roadmap
Click any topic to jump in
Perplexity
The fundamental metric for language model quality — how surprised the model is by text.
BLEU & ROUGE
Reference-based metrics for translation (precision) and summarization (recall).
Human Evaluation
When and how to use human judges for nuanced quality assessment.
From metrics to holistic evaluation
Benchmark Suites
Standardized tests across reasoning, knowledge, coding, and safety capabilities.
Red Teaming
Adversarial testing to find failure modes and safety vulnerabilities before users do.
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