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Two papers explore transformer generalization and positional encoding

By PulseAugur Editorial · [2 sources] ·

Two recent arXiv papers delve into the generalization capabilities of transformer models. The first paper investigates how different positional encoding schemes, such as RoPE and ALiBi, affect a transformer's ability to handle varying inter-token distances, a concept termed distance generalization. The second paper focuses on establishing theoretical bounds for generalization error in single-layer transformers, proposing improvements that are independent of input sequence length and offer a better decay rate with increasing sample size. AI

IMPACT These papers contribute to a deeper theoretical understanding of transformer model limitations and potential improvements in handling diverse data distributions.

RANK_REASON Two academic papers published on arXiv discussing transformer model generalization.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Two papers explore transformer generalization and positional encoding

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Two academic papers published on arXiv discussing transformer model generalization.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Daniel Henrik Nevermann, Claudius Gros ·

    Distance generalization in transformers: why bother with positional encoding?

    arXiv:2609.11913v1 Announce Type: new Abstract: Out-of-distribution length generalization, namely to extrapolate a task from short to longer context, has been studied intensively for transformers. Here we focus on distance generalization, which probes performance when inter-token…

  2. arXiv cs.LG TIER_1 English(EN) · Lan V. Truong ·

    On Generalisation Error Bounds for Transformers

    arXiv:2410.11500v2 Announce Type: replace-cross Abstract: In this paper, we establish a collection of covering number bounds for linear function classes under various norm constraints on the inputs and matrices. We then combine these results with existing covering number bounds t…

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