Perturbation is All You Need for Extrapolating Language Models

• URL: http://arxiv.org/abs/2605.04344v1
• Date: Tue, 05 May 2026 23:03:33 GMT
• Title: Perturbation is All You Need for Extrapolating Language Models
• Abstract summary: We introduce a simple yet powerful framework for training large language models.<n>We develop a rigorous theory of extrapolability, namely, the capacity of a model class to make reliable predictions.<n>Results show that the proposed method consistently improves out-of-support prediction while maintaining competitive in-support performance.
• Score: 10.671612115533597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple yet powerful framework for training large language models. In contrast to the standard autoregressive next-token prediction based on an exact prefix, we propose a perturbation-based procedure that first transforms the prefix into a semantic neighbor 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 training corpus. We evaluate the finite-sample performance of the proposed procedure using both synthetic and real-world language data. Results show that the proposed method consistently improves out-of-support prediction while maintaining competitive in-support performance, demonstrating that perturbation offers a practical route to language modeling.

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