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).- Anthology ID:
- 2022.nlp4dh-1.18
- Volume:
- Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities
- Month:
- November
- Year:
- 2022
- Address:
- Taipei, Taiwan
- Venue:
- NLP4DH
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 145–154
- Language:
- URL:
- https://aclanthology.org/2022.nlp4dh-1.18
- DOI:
- Cite (ACL):
- Irene Russo. 2022. Creative Text-to-Image Generation: Suggestions for a Benchmark. In Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities, pages 145–154, Taipei, Taiwan. Association for Computational Linguistics.
- Cite (Informal):
- Creative Text-to-Image Generation: Suggestions for a Benchmark (Russo, NLP4DH 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.nlp4dh-1.18.pdf