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Computer Science > Computation and Language

arXiv:2309.07315 (cs)
[Submitted on 13 Sep 2023 (v1), last revised 19 Sep 2023 (this
version, v2)]

Title:Traveling Words: A Geometric Interpretation of Transformers

Authors:Raul Molina
Download a PDF of the paper titled Traveling Words: A Geometric
Interpretation of Transformers, by Raul Molina
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    Abstract:Transformers have significantly advanced the field of
    natural language processing, but comprehending their internal
    mechanisms remains a challenge. In this paper, we introduce a
    novel geometric perspective that elucidates the inner mechanisms
    of transformer operations. Our primary contribution is
    illustrating how layer normalization confines the latent features
    to a hyper-sphere, subsequently enabling attention to mold the
    semantic representation of words on this surface. This geometric
    viewpoint seamlessly connects established properties such as
    iterative refinement and contextual embeddings. We validate our
    insights by probing a pre-trained 124M parameter GPT-2 model. Our
    findings reveal clear query-key attention patterns in early
    layers and build upon prior observations regarding the
    subject-specific nature of attention heads at deeper layers.
    Harnessing these geometric insights, we present an intuitive
    understanding of transformers, depicting them as processes that
    model the trajectory of word particles along the hyper-sphere.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2309.07315 [cs.CL]
          (or arXiv:2309.07315v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2309.07315
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Raul Molina [view email]
[v1] Wed, 13 Sep 2023 21:01:03 UTC (1,805 KB)
[v2] Tue, 19 Sep 2023 00:34:56 UTC (1,805 KB)
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