LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
The paper explores the effectiveness of language-pretrained transformers in forecasting time series data. It demonstrates that pretraining creates a reusable manifold that facilitates cross-modal transfer, allowing for competitive forecasts without paired supervision. The findings suggest that finetuning aligns existing directions rather than starting from scratch, enhancing optimization and performance.
- ▪Language-pretrained transformers can effectively forecast time series data.
- ▪Cross-modal transfer occurs due to a reusable manifold created during pretraining.
- ▪Finetuning aligns existing directions, improving optimization and performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20449 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.20449 (cs) [Submitted on 19 May 2026] Title:LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series Authors:Alexis Roger, Prateek Humane, Zhenghan Tai, Gwen Legate, Andrei Mircea, Vasilii Feofanov, Irina Rish View a PDF of the paper titled LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series, by Alexis Roger and 6 other authors View PDF HTML (experimental) Abstract:Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because language pretraining preconditions time series training with a reusable manifold.
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