What Are AI Benchmarks?
Benchmarks are standardized tests that measure AI capabilities. They help compare different models, track progress over time, and identify strengths and weaknesses.
Take With Caution
Benchmarks measure specific tasks. High scores don't mean an AI is "smarter" overall—and models often train specifically to do well on benchmarks.
Common LLM Benchmarks
MMLU (Massive Multitask Language Understanding)
57 academic subjects from elementary to professional level. Tests general knowledge.
HumanEval
Programming problems that test code generation ability.
GSM8K
Grade school math word problems. Tests mathematical reasoning.
HellaSwag
Sentence completion that requires commonsense reasoning.
TruthfulQA
Questions designed to test whether models produce truthful answers vs. plausible-sounding falsehoods.
Human-Comparative Tests
- Bar Exam — GPT-4 scored 90th percentile
- SAT — Top models score near-perfect
- Medical licensing exams — Passing scores on USMLE
- AP exams — High scores on various subjects
Benchmark Leaderboards
- Chatbot Arena — Human preference rankings through blind comparisons
- Open LLM Leaderboard — Hugging Face benchmark aggregation
- LMSYS — Crowdsourced model comparisons
Problems with Benchmarks
- Data contamination — Models may have seen test questions in training
- Gaming — Companies optimize specifically for benchmark scores
- Narrow focus — Real-world tasks are more complex than benchmarks
- Saturation — Models near-perfect on many old benchmarks
- Bias — Benchmarks reflect their creators' assumptions
What Benchmarks Can't Measure
- Creativity and originality
- Common sense in real situations
- Emotional intelligence
- Long-term reasoning
- Practical usefulness
Summary
- • Benchmarks are standardized tests for comparing AI models
- • Common benchmarks: MMLU, HumanEval, GSM8K, TruthfulQA
- • Problems: data contamination, gaming, narrow focus
- • Real-world usefulness is harder to measure than benchmark scores