LLM Evaluation and Benchmarks

Why Evaluation Matters Rigorous evaluation ensures LLMs perform as expected on target tasks, helps compare models, and identifies areas for improvement.

Standard Benchmarks

Knowledge and Reasoning

Code and Math

Conversation and Instruction

Evaluation Metrics

Automatic Metrics

• Perplexity: Lower is better (language modeling quality)
• Pass@k: Probability of correct code in k attempts
• BLEU/ROUGE: Text similarity (limited usefulness for LLMs)
• Exact Match: For factual or extraction tasks

Human Evaluation

• Preference rankings: A vs B comparisons
• Likert scales: Quality ratings (1-5)
• Task success rate: Binary completion metrics
• LLM-as-Judge: Use GPT-4 or Claude to evaluate outputs

Best Practices 1. Use multiple benchmarks across capabilities 2. Include domain-specific evaluations for your use case 3. Combine automatic metrics with human judgment 4. Test for safety and edge cases, not just accuracy 5. Version evaluation sets and track performance over time