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An Attentive Survey of Attention Models

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arxiv 1904.02874 v3 pith:EQTTRFPU submitted 2019-04-05 cs.LG stat.ML

classification cs.LGstat.ML
keywords attentionbeenneuralsurveyapplicationsdiscussmodelingmodels
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Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated, and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.

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Cited by 2 Pith papers

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    cs.CL 2025-08 reject novelty 5.0 of 10

    The paper argues that LLM next-token distributions form Markov categories whose paraphrase equivalences can be studied by homotopy theory, but its main theorem is unsupported.

  2. Topos Theory for Generative AI and LLMs

    cs.AI 2025-08 reject novelty 5.0 of 10

    The paper claims the category of LLM functions is a topos and uses that to propose new compositional architectures like pullbacks, pushouts, and subobject classifiers, but gives no implementation or complete proofs.

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