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    Language, speech & LLMs· 2018

    Universal Language Model Fine-tuning for Text Classification

    Jeremy Howard (first author) · 2018

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    DOI 10.18653/v1/p18-1031arXiv 1801.06146OpenAlex W2798812533

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    Why it matters

    Demonstrated general-purpose language model fine-tuning before Transformer-based transfer learning dominated.

    Condensed from the expert lists' notes.

    The record

    First released
    2018 (from arXiv 1801.06146)
    Citations
    4,403 citations, as counted by Semantic Scholar logoSemantic Scholar (non-commercial licence)
    Named by
    both expert lists
    Score
    0.349 · computed rank 482 · consensus 0.41, must-read 0, citations percentile 0.644 (2010s · language), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and Semantic Scholar logoSemantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.