@inproceedings{russo-2022-creative,
    title = "Creative Text-to-Image Generation: Suggestions for a Benchmark",
    author = "Russo, Irene",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Alnajjar, Khalid  and
      Partanen, Niko  and
      Rueter, Jack},
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.nlp4dh-1.18/",
    doi = "10.18653/v1/2022.nlp4dh-1.18",
    pages = "145--154",
    abstract = "Language models for text-to-image generation can output good quality images when referential aspects of pictures are evaluated. The generation of creative images is not under scrutiny at the moment, but it poses interesting challenges: should we expect more creative images using more creative prompts? What is the relationship between prompts and images in the global process of human evaluation? In this paper, we want to highlight several criteria that should be taken into account for building a creative text-to-image generation benchmark, collecting insights from multiple disciplines (e.g., linguistics, cognitive psychology, philosophy, psychology of art)."
}