Papers by Hadar Mulian
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data (2026.findings-acl)
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Ofir Arviv, Kristjan Greenewald, Yotam Perlitz, Hadar Mulian, Michal Shmueli-Scheuer, Leshem Choshen
| Challenge: | Current evaluation practices, typically employing fixed-size benchmarks, are inherently wasteful, continuing to the predetermined sample size even when the CI reaches 2.5, saving 80% of the evaluation cost. |
| Approach: | They propose an adaptive evaluation framework that combines sequential testing with stopping criteria tailored to common evaluation needs such as diminishing returns detection and minimum detectable effect size. |
| Outcome: | The proposed framework reduces computational cost and reliability while maintaining statistical significance. |