Prompt-based Generation

Prompt-Based Generation is a text generation approach that belongs to the broader class of natural language generation methods, differentiated by its reliance on user-provided prompts as the primary conditioning input to guide the generation process of pre-trained language models.

255

Publications

26.5K

Citations

1.1K

Authors

267

Institutions

Publications per year

2017–2026

250

Authors

1.1K

  1. GL
    Ge Li

    Peking University, China

    5 publications · 255 citations

    5

    h-index

  2. PD
    Paul Denny

    University of Auckland, New Zealand

    4 publications · 297 citations

    4

    h-index

  3. ZL
    Zhiyuan Liu

    Tsinghua University, China

    4 publications · 307 citations

    4

    h-index

  4. ZJ
    Zhi Jin

    Peking University, China

    4 publications · 243 citations

    4

    h-index

  5. JL
    Juho Leinonen

    Aalto University, Finland

    4 publications · 297 citations

    4

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 78 publications · 8.3K citations

    29

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 344 citations

    8

    h-index

  3. ACM Transactions on Software Engineering and Methodology

    TOSEM · 3 publications · 249 citations

    3

    h-index

  4. ACM Transactions on Graphics

    TOG · 3 publications · 509 citations

    3

    h-index

  5. 3

    h-index

1–5