Language Model Fine-tuning

Language Model Fine-Tuning is a machine learning adaptation procedure in which a pre-trained language model undergoes additional training on a domain-specific or task-specific dataset to refine its parameters for improved performance on targeted downstream applications.

839

Publications

116.9K

Citations

4.2K

Authors

600

Institutions

Publications per year

2017–2026

828

Authors

4.2K

  1. ZL
    Zhiyuan Liu

    Tsinghua University, China

    23 publications · 2.7K citations

    19

    h-index

  2. MS
    Maosong Sun

    Tsinghua University, China

    15 publications · 2K citations

    14

    h-index

  3. JL
    Juanzi Li

    Tsinghua University, China

    11 publications · 1.8K citations

    11

    h-index

  4. LZ
    Luke Zettlemoyer

    University of Washington, United States

    11 publications · 17.9K citations

    11

    h-index

  5. SR
    Sebastian Ruder

    Google (United States), United States

    11 publications · 1.2K citations

    10

    h-index

1–5 of 4.2K

Venues

  1. arXiv (Cornell University)

    Repository · 318 publications · 44.4K citations

    77

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 24 publications · 1.3K citations

    17

    h-index

  3. 15

    h-index

  4. 14

    h-index

  5. IEEE Access

    10 publications · 405 citations

    10

    h-index

1–5