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Computer Science > Computation and Language

arXiv:2405.05417 (cs)
[Submitted on 8 May 2024]

Title:Fishing for Magikarp: Automatically Detecting Under-trained
Tokens in Large Language Models

Authors:Sander Land, Max Bartolo
View a PDF of the paper titled Fishing for Magikarp: Automatically
Detecting Under-trained Tokens in Large Language Models, by Sander
Land and 1 other authors
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    Abstract:The disconnect between tokenizer creation and model
    training in language models has been known to allow for certain
    inputs, such as the infamous SolidGoldMagikarp token, to induce
    unwanted behaviour. Although such `glitch tokens' that are
    present in the tokenizer vocabulary, but are nearly or fully
    absent in training, have been observed across a variety of
    different models, a consistent way of identifying them has been
    missing. We present a comprehensive analysis of Large Language
    Model (LLM) tokenizers, specifically targeting this issue of
    detecting untrained and under-trained tokens. Through a
    combination of tokenizer analysis, model weight-based indicators,
    and prompting techniques, we develop effective methods for
    automatically detecting these problematic tokens. Our findings
    demonstrate the prevalence of such tokens across various models
    and provide insights into improving the efficiency and safety of
    language models.

Comments:   16 pages, 4 figures. For associated code, see this https
            URL
Subjects:   Computation and Language (cs.CL)
MSC         68T50
classes:
Cite as:    arXiv:2405.05417 [cs.CL]
            (or arXiv:2405.05417v1 [cs.CL] for this version)

Submission history

From: Sander Land [view email]
[v1] Wed, 8 May 2024 20:37:56 UTC (5,003 KB)
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    Automatically Detecting Under-trained Tokens in Large Language
    Models, by Sander Land and 1 other authors
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