Fact-checked Text Generation

Fact-Checked Text Generation is a language generation framework that integrates verifiability mechanisms to ensure generated textual content aligns with trusted factual knowledge sources, thereby distinguishing it from conventional text generation approaches that prioritize fluency and coherence without explicit accuracy guarantees.

18

Publications

997

Citations

78

Authors

32

Institutions

Publications per year

2017–2026

17

Authors

78

  1. IA
    Isabelle Augenstein

    University of Copenhagen, Denmark

    3 publications · 194 citations

    3

    h-index

  2. TC
    Tanmoy Chakraborty

    Indraprastha Institute of Information Technology Delhi, India

    2 publications · 156 citations

    2

    h-index

  3. MC
    Meeyoung Cha

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 156 citations

    2

    h-index

  4. RD
    Renée DiResta

    Stanford University, United States

    2 publications · 156 citations

    2

    h-index

  5. EF
    Emilio Ferrara

    University of Southern California, United States

    2 publications · 156 citations

    2

    h-index

1–5 of 78

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 111 citations

    4

    h-index

  2. 1

    h-index

  3. CLEF (Working Notes)

    1 publications · 15 citations

    1

    h-index

  4. 1

    h-index

  5. Nature Machine Intelligence

    1 publications · 123 citations

    1

    h-index

1–5