Fine-tuning language models on synthetic distribution-sampling prompts improves their ability to generate outputs that match target probability distributions on held-out cases.
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Probabilistic Calibration Is a Trainable Capability in Language Models
Fine-tuning language models on synthetic distribution-sampling prompts improves their ability to generate outputs that match target probability distributions on held-out cases.