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The labeled-data crossover N*, the training-set size at which a trained classical model's learning curve overtakes a frozen LLM's training-free (and therefore flat) error, is quantified and found that training wins fast.
LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or"mixture-of-experts"(Shazeer et al., 2017) panels of judges. These systems share a key assumption: that consistency -- agreement among judges, or among a mode...
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