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# Stop Evaluating Models Like It's the 50s - Alejandro Vidal, Mindmakers

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 23 min read · 1 upvotes · 0 comments

## Summary

A talk proposing the application of Item Response Theory (IRT) from psychometrics to improve LLM benchmark evaluation. Instead of simply counting correct answers (classical test theory), IRT assigns each benchmark question a difficulty parameter (B) and discrimination parameter (A), and estimates each model's intelligence level (theta) as a distribution with confidence intervals. Practical applications demonstrated include: auditing benchmarks to detect mislabeled or negatively discriminating questions, reducing benchmark size by up to 5x while maintaining 99% ranking correlation, detecting benchmark contamination/overfitting via residual analysis, protecting benchmarks using adaptive testing with fingerprint item sets, identifying bias between open-weight and closed-weight models, and clustering models by residual correlations to detect distillations and model lineage.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=O3FEoMYvUf8>

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