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Aggregation metrics in benchmarks can mask the irreplaceable strengths of models, leading to a misleading evaluation of their true capabilities.
Non-imperative syntactic structures can undermine safety alignment in large language models, exposing them to sophisticated jailbreaks.
Systematic feature engineering can outperform advanced model architectures, closing a critical evaluation gap in tabular benchmarks.
