Quantifying Ranking Uncertainty in LLM Benchmarks
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Computer Science > Machine Learning
Title:Quantifying Ranking Uncertainty in LLM Benchmarks
Abstract:Pretrained models are typically ranked on multi-task leaderboards to assess their effectiveness across diverse tasks. Rank confidence intervals were recently introduced as a method to quantify the uncertainty in these rankings by aggregating pairwise hypothesis tests. In this work, we analyze the sources of uncertainty in the knowledge evaluation benchmark MMLU and show how hypothesis tests can be modified to account for their effects. We demonstrate that ranking variability across MMLU subjects is substantial and should be considered when comparing LLMs or identifying the top-performing models.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Machine Learning (stat.ML) |
| Cite as: | arXiv:2607.16259 [cs.LG] |
| (or arXiv:2607.16259v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16259
arXiv-issued DOI via DataCite (pending registration)
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