Revamping AI Evaluation with IRT
AI Engineer · youtube · 2026-07-14
This talk argues that AI evaluation should move past "1950s-style" simple accuracy statistics and instead adopt **IRT (Item Response Theory)** and adaptive testing from psychometrics. ### Core Views - Traditional evaluations treat every question as equally important, merely calculating the percentage of correct answers, which yields very low information. - IRT places questions on a unified ability scale and provides more realistic error margins. - Adaptive testing can measure the same ability using fewer questions, enabling harder-to-contaminate private or rotating benchmarks. - Evaluation distributions can also expose **data leakage**, question noise, and whether a model's capabilities are robust or just based on luck. ### Conclusion The author believes this approach makes evaluations cheaper, harder to game, and much better at telling us what the model actually learned, rather than just highlighting a high score.
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