@inproceedings{althani-etal-2024-using,
    title = "Using In-context Learning to Automate {AI} Image Generation for a Gamified Text Labelling Task",
    author = "Althani, Fatima  and
      Madge, Chris  and
      Poesio, Massimo",
    editor = "Madge, Chris  and
      Chamberlain, Jon  and
      Fort, Karen  and
      Kruschwitz, Udo  and
      Lukin, Stephanie",
    booktitle = "Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.games-1.4",
    pages = "21--31",
    abstract = "This paper explores a novel automated method to produce AI-generated images for a text-labelling gamified task. By leveraging the in-context learning capabilities of GPT-4, we automate the optimisation of text-to-image prompts to align with the text being labelled in the part-of-speech tagging task. As an initial evaluation, we compare the optimised prompts to the original sentences based on imageability and concreteness scores. Our results revealed that optimised prompts had significantly higher imageability and concreteness scores. Moreover, to evaluate text-to-image outputs, we generate images using Stable Diffusion XL based on the two prompt types, optimised prompts and the original sentences. Using the automated LIAON-Aesthetic predictor model, we assigned aesthetic scores for the generated images. This resulted in the outputs using optimised prompts scoring significantly higher in predicted aesthetics than those using original sentences as prompts. Our preliminary findings suggest that this methodology provides significantly more aesthetic text-to-image outputs than using the original sentence as a prompt. While the initial results are promising, the text labelling task and AI-generated images presented in this paper have yet to undergo human evaluation.",
}

This paper explores a novel automated method to produce AI-generated images for a text-labelling gamified task. By leveraging the in-context learning capabilities of GPT-4, we automate the optimisation of text-to-image prompts to align with the text being labelled in the part-of-speech tagging task. As an initial evaluation, we compare the optimised prompts to the original sentences based on imageability and concreteness scores. Our results revealed that optimised prompts had significantly higher imageability and concreteness scores. Moreover, to evaluate text-to-image outputs, we generate images using Stable Diffusion XL based on the two prompt types, optimised prompts and the original sentences. Using the automated LIAON-Aesthetic predictor model, we assigned aesthetic scores for the generated images. This resulted in the outputs using optimised prompts scoring significantly higher in predicted aesthetics than those using original sentences as prompts. Our preliminary findings suggest that this methodology provides significantly more aesthetic text-to-image outputs than using the original sentence as a prompt. While the initial results are promising, the text labelling task and AI-generated images presented in this paper have yet to undergo human evaluation.
Althani, Fatima Madge, Chris Poesio, Massimo
Using In-context Learning to Automate AI Image Generation for a Gamified Text Labelling Task