Probabilistic Calibration Is a Trainable Capability in Language Models
This study investigates whether fine-tuning language models can improve their ability to follow specific probability distributions when generating text, even for implicit constraints. Researchers compared two fine-tuning methods—soft-target and hard-target—using synthetic mathematical prompts. Both methods significantly improved structured sampling fidelity on unseen data and extended gains to broader random generation tasks, suggesting probabilistic calibration is a trainable skill.