arXiv AI
2d ago

Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones

The paper presents quantitative generalization bounds for fixed‑precision transformer models, addressing both in‑distribution statistical generalization and length generalization. It derives guarantees based on model parameters, quantization space, and alphabet size, and shows how training length and sample size trade off, with longer training being exponentially more efficient. Experiments confirm the theoretical predictions.

By Marius Henry, Michael Rizvi-Martel, Michael Hahn
arXiv Machine Learning
Sep 10

Length Generalization for Transformers via Compression

arXiv:2609.08851v1 Announce Type: new Abstract: Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particul...

By Georg Zetzsche, Hongjian Jiang, Andy Yang, Pascal Bergstr\"a{\ss}er, Marco S\"alzer, David Chiang, Anthony W. Lin