Every AI model release comes with benchmark numbers. But what do they actually mean?
Common Benchmarks
- MMLU: Measures knowledge across 57 subjects
- GSM8K: Math word problems
- HumanEval: Coding tasks
- HellaSwag: Common sense reasoning
What They Measure
Benchmarks test specific capabilities under controlled conditions. They're useful but limited.
The Limitations
- Benchmarks don't capture real-world usage
- Models can be optimized for benchmarks
- Benchmarks don't measure safety or bias
- Performance varies by task
How to Read Them
- Look at multiple benchmarks, not just one
- Consider the context and use case
- Remember: benchmarks are indicators, not guarantees
- Test in your own environment
Why This Matters
Benchmarks help compare models, but they're not the whole story. Real-world performance matters more.
The Takeaway
Use benchmarks as a starting point, not the final answer. Test models in your own context. That's where you'll see real performance.