Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE.Inf
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This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.
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Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
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Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.