LLM Capability Elicitation and Evaluation is the systematic process of measuring what a language model can and cannot do — including prompt engineering for evaluation, avoiding contamination, and interpreting benchmark results correctly.

The Evaluation Challenge

• LLMs are sensitive to prompt formatting — same capability, different prompt → different score.
• Benchmark contamination: Training data may include test examples.
• Prompt sensitivity: "Answer:" vs. "The answer is:" can change accuracy by 10%.
• True vs. elicited capability: Model may know but fail to express correctly.

Evaluation Methodologies

Few-Shot Prompting for Evaluation:

• Include K examples in prompt before the test question.
• K=0 (zero-shot): Tests true generalization.
• K=5 (5-shot): Helps model understand format — reveals more capability.
• GPT-3 paper: 5-shot outperforms 0-shot by 20+ points on many benchmarks.

Chain-of-Thought Evaluation:

• Complex reasoning: CoT prompting ("think step by step") reveals reasoning.
• Direct answer vs. CoT: 65% → 92% on GSM8K for GPT-4.

Contamination Detection

• n-gram overlap: Check if test questions appear in training data.
• Membership inference: Does model complete test examples unusually well?
• Dynamic benchmarks: New questions generated after model's training cutoff.
• LiveBench: Continuously updated benchmark with recent data.

Evaluation Dimensions

Human Evaluation

• Automated benchmarks miss: Fluency, creativity, factual grounding, tone.
• Chatbot Arena (LMSYS): Blind pairwise comparison — Elo rating from human preferences.
• Most reliable ranking but expensive and slow.

Robust LLM evaluation is a critical and unsolved problem in AI — with models increasingly exceeding benchmark saturation, understanding the gap between benchmark performance and real-world capability requires ever more sophisticated evaluation methodologies that resist gaming and contamination.