Probabilistic Calibration Is a Trainable Capability in Language Models
Language models are increasingly used in settings where outputs should follow an intended distribution, yet their generation probabilities are often poorly calibrated to it. In many such requests the distribution is implicit and the valid outputs cannot be listed in advance, so the task cannot be passed to an external random number generator. We study whether this capability can be improved directly through fine-tuning. Concretely, we fine-tune language models on synthetic prompts that require sampling from mathematical distributions, and compare two Calibration Fine-Tuning variants: a soft-target method that converts the desired output distribution into trie-derived next-token targets, a...
| Authors | Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 May 2026 |
| Last updated at source | 9 October 2026 |
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