arXiv cs.AI· Francesco I. Re, Shubhangi Ghosh, Tim Vieira, Ryan Cotterell·· 9 天前AI 评分20
利用势函数高效估计自回归语言模型下的期望值
Estimating Great Expectations Under Autoregressive Language Models with Potentials
AI 导读
研究者提出用势函数（prefix 上的实值函数）分解测试泛函，利用采样时附带产生的下一 token 条件概率来降低自回归语言模型期望估计的方差。该方法给出了势函数降低方差的条件，并为多种估计目标设计了实用势函数，在相当计算成本下实现了显著方差缩减。
正文 · 原文
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, and derive conditions under which a potential reduces this variance. We then develop practical potentials for several estimands and applications, and demonstrate substantial variance reductions across several estimands at comparable computational cost.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME) |
| Cite as: | arXiv:2610.11399 [cs.AI] |
| (or arXiv:2610.11399v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11399 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Francesco Ignazio Re [view email]
[v1]
Thu, 8 Oct 2026 07:30:43 UTC (1,809 KB)
来源：arXiv cs.AI · arxiv.org