Model Evaluation

Model evaluation assesses how well AI models perform on various tasks and criteria.

• **Key Metrics:**
• Accuracy: Correct predictions / total
• Perplexity: Language model uncertainty
• BLEU/ROUGE: Text similarity scores
• F1 Score: Balance precision/recall
• Human preference: User studies, Elo ratings

Popular Benchmarks: - MMLU (knowledge) - HumanEval (coding) - MT-Bench (conversation) - BigBench (diverse tasks) - HELM (holistic evaluation)

Evaluation Challenges: - Benchmark contamination - Gaming metrics - Real-world performance gap - Emergent capabilities

Best Practices: - Multiple benchmarks - Human evaluation - Task-specific metrics - Regular re-evaluation