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Cheap auxiliary signals can provide unbiased conditional performance estimates for language models, revealing hidden strengths and weaknesses that gold labels alone miss.
Reduce testing costs without compromising predictive accuracy by learning cost-optimal sequential decision policies from retrospective data, even with informative missingness.
Stop forcing all your modalities into a shared-private representation: HCL learns which modalities *actually* share information, leading to better representations and predictions.
