arXiv:2605.24818v1 Announce Type: cross Abstract: The literature on test set contamination largely focuses on detection, but the correction of contaminated test scores is underexplored. Our core proposal is to spike the training data by intentionally contaminating some test examples at known rates. The spiked examples can then be used to calibrate predictors of model memorization which enable principled statistical correction of inflated test scores. To evaluate different correction estimators, we first present a simulation framework based on the Hubble models. Hubble models come in minimal pa
The increasing sophistication and scale of AI models make test set contamination a more prevalent and complex issue, requiring robust methods for correction rather than just detection.
Contaminated test sets lead to inflated and misleading performance metrics for AI models, hindering accurate evaluation, deployment, and trust in AI systems across various applications.
This proposal shifts the focus from merely detecting test set contamination to actively correcting for its effects, potentially leading to more reliable and trustworthy AI performance assessments.
- · AI researchers
- · AI developers
- · AI ethics organizations
- · High-stakes AI applications
- · AI models with subtly contaminated training data
- · Organizations relying on uncorrected, inflated AI benchmarks
More accurate and reliable benchmarks for AI model performance become possible.
Increased trust in AI model evaluations and a clearer understanding of true model capabilities.
Reduced risk of deploying over-estimated AI systems in critical applications, leading to better real-world outcomes and potentially accelerated AI adoption in sensitive sectors.
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Read at arXiv cs.CL