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Transformers lack computable length generalization bounds

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated that computable length generalization bounds for transformers are not possible, even with just two layers. This finding addresses an open problem in machine learning, indicating that predicting a transformer's generalization performance on inputs of varying lengths from finite training data is fundamentally limited. The study also provides a computable bound for a restricted subset of transformer languages, equivalent to fixed-precision transformers, showing their length complexity is exponential. AI

IMPACT Confirms theoretical limits on transformer generalization, potentially guiding future research toward alternative architectures or training methods.

RANK_REASON Academic paper detailing theoretical limitations of transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Transformers lack computable length generalization bounds

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Academic paper detailing theoretical limitations of transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andy Yang, Pascal Bergstr\"a{\ss}er, Georg Zetzsche, David Chiang, Anthony W. Lin ·

    Length Generalization Bounds for Transformers

    arXiv:2603.02238v2 Announce Type: replace Abstract: Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite training data. To provide such a guarantee, one needs to be able to compute a leng…

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