B
Benchmark
Standardised tests used to evaluate and compare AI model performance across specific tasks or capabilities.
In-Depth Explanation
Benchmarks are standardised evaluation datasets and metrics used to measure and compare AI model capabilities. They provide objective measures for comparing different models and tracking progress.
Common AI benchmarks:
- MMLU: Multitask language understanding (57 subjects)
- HellaSwag: Commonsense reasoning
- HumanEval: Code generation
- GSM8K: Grade school math
- ARC: Science reasoning
- TruthfulQA: Factual accuracy
- WinoGrande: Coreference resolution
Benchmark considerations:
- Leakage: Test data in training data
- Overfitting: Optimising for benchmark vs real performance
- Validity: Does benchmark measure what matters?
- Saturation: Benchmark too easy for current models
- Coverage: What capabilities aren't tested?
Using benchmarks:
- Compare models for your use case
- Track improvement over versions
- Identify specific strengths/weaknesses
- Set performance baselines
Business Context
Benchmarks help compare models when selecting AI for business use, though real-world performance may differ from benchmark scores.
How Clever Ops Uses This
We use benchmarks to guide model selection for Australian businesses, supplemented by testing on actual use case data.
Example Use Case
"Comparing Claude, GPT-4, and Llama on code generation benchmarks when selecting a model for a developer tools product."