arXiv:2605.20988v1 Announce Type: new Abstract: We study transformers' generalization behavior on boolean domains from the perspective of the Fourier Spectra of their target functions. In contrast to prior work (Edelman et al., 2022; Trauger and Tewari, 2024), which derived generalization bounds from Rademacher complexity, we investigate the feasibility of obtaining generalization bounds via PAC-Bayes theory. We show that sparse spectra concentrated on low-degree components enable low-sharpness constructions with good generalization properties. Our idea is to show the existence of flat minima
The continuous advancements in AI research, particularly deep learning models like transformers, necessitate ongoing investigation into their fundamental properties and limitations to improve performance and reliability.
Understanding the generalization behavior of transformers, especially through new theoretical frameworks, is crucial for developing more robust and efficient AI models with predictable performance in real-world applications.
This research introduces a novel theoretical approach (PAC-Bayes theory, Fourier Spectra) to explaining transformer generalization, moving beyond previous Rademacher complexity-based bounds, potentially leading to new model design principles.
- · AI researchers
- · Deep learning practitioners
- · SaaS companies leveraging AI
- · Companies relying on transformer-based models
- · Developers of less robust AI models
- · Theories relying solely on older generalization bounds
Improved theoretical understanding of transformer generalization and robustness.
Development of new transformer architectures and training methods that exploit these theoretical insights for better real-world performance.
Acceleration of AI adoption in critical domains due to increased trust and predictability of advanced models.
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Read at arXiv cs.LG