Perturbation is All You Need for Extrapolating Language Models
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard autoregressive next-token prediction based on an exact prefix, we introduce a perturbation-based procedure that first transforms the prefix into a semantic neighbour and then conditions on this perturbed variant for next-token prediction. This yields a hierarchical model with a pre-post-additive noise structure. Within this framework, we develop a rigorous theory of extrapolability, namely, the capacity of a model class to make reliable predictions for token sequences that lie outside the empirical support of the tr...
| Authors | Zetai Cen, Jin Zhu, Xinwei Shen et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 20 July 2026 |
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