Conformal Nucleus Sampling

Understand

Language models generate text based on successively sampling the next word.

• A decoding procedure based on nucleus (top-$p$) sampling chooses from the smallest possible set of words whose cumulative probability exceeds the probability $p$.
• In this work, we assess whether a top-$p$ set is indeed aligned with its probabilistic meaning in various linguistic contexts.
• We employ conformal prediction, a calibration procedure that focuses on the construction of minimal prediction sets according to a desired confidence level, to calibrate the parameter $p$ as a function of the entropy of the next word distribution.