Estimating great expectations under autoregressive language models with potentials
Quick summary
arXiv:2610.11399v1 Announce Type: new 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 the test functional additively. We construct an estimator whose variance depends on the chosen potential, a
Key takeaways
- arXiv:2610.11399v1 Announce Type: new 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.
Why it matters
“Estimating great expectations under autoregressive language models with potentials” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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