Unveiling the Rigged AI Benchmark Game: Why Leaders Control Billions and Mind Share

Get ready to blow the lid off the AI benchmarks game, where rigged systems and creative tricks have led to a crisis of trust - but there's a better way to measure what matters.

1. Darius, CEO of Scorecard, discusses how AI benchmarks can be rigged and incentivize companies to keep it that way.
2. A benchmark is composed of a model, test set, and metric; multiple evaluations are bundled together and standardized to make them comparable.
3. Benchmarks control billions in market value and mind share, influencing investment decisions, public perception, enterprise contracts, and developer mind share.
4. High benchmark scores can define market leaders and destroy competitors, creating high stakes that lead to creative ways to win.
5. Common tricks include making apple-to-orange comparisons, getting privileged access to test questions, and optimizing for style over substance.
6. XAI compared their best configuration against other models' standard configurations, which is misleading; they didn't show OpenAI 03's high performance at consensus 64.
7. Frontier Math was supposed to be a super secret benchmark, but OpenAI funded it and got privileged access to the data set, creating a trust problem.
8. Companies are training models to be charming instead of accurate, focusing on engagement rather than actual performance.
9. Human SATs have a similar issue, where essay length contributes to score variance; 39% of score variance in SAT scores is due to essay length.
10. When a measure becomes a target, it ceases to be a good measure; benchmarks should not be treated as targets worth billions.
11. Experts like Andre Krothy (co-founder of OpenAI) and John Yang (creator of Sweetbench) have expressed concerns about the current evaluation crisis.
12. To fix public metrics, all three components - model comparisons, test sets, and metrics - need to be addressed.
13. Apple comparisons should be required, with the same computational budget and constraints, and transparent cost-performance trade-offs.
14. Test sets need transparency, with open-sourced data, methodologies, and code, and regular rotation of test questions to prevent overfitting.
15. Metrics should control for style effects and require all attempts to be public, eliminating the possibility of cherry-picking the best run.
16. Independent benchmarks in specific domains are emerging, including legal bench medqa, fintech, agent eval, and better bench.
17. To truly win at evaluations, focus on building a set of evaluations that matter for your use case instead of chasing public benchmarks.
18. Gather real data from five actual queries in your production system, as they are worth more than 100 academic questions.
19. Choose metrics based on quality, cost, and latency that are relevant to your specific application; a chatbot requires different metrics than a medical diagnosis system.
20. Test the top five models on your specific data, not just those leading generic benchmarks.
21. Systematize evaluation with consistent, repeatable methods, either by building it yourself or using platforms like Scorecard.
22. Make evaluation a continuous process, iterating and improving based on feedback and results before deployment.
23. Pre-eployment evaluation loops separate teams that ship reliable AI from those constantly fighting production issues.
24. While all benchmarks are wrong in some ways, some can be useful; the key is to know which ones are meaningful for your purposes and build evaluations that actually help you ship better products.

Source: AI Engineer via YouTube

❓ What do you think? What is the most effective way to evaluate AI systems, and why do traditional benchmarks often fall short in capturing meaningful performance metrics? Feel free to share your thoughts in the comments!