Estimating Great Expectations Under Autoregressive Language Models with Potentials
arXiv:2610.11399v2 Announce Type: replace Abstract: Many applications of language models hinge not on individual samples but on the expectation of a test functional under the model. Estimating such expectations reliably can be computationally expensive. In this paper, we show how to make estimation more efficient by exploiting the next-token conditional probabilities which are available as a by-product of sampling. We do so through potentials: real-valued functions on prefixes that decompose th…