@inproceedings{madina-etal-2024-preliminary,
    title = "A Preliminary Study of {C}hat{GPT} for {S}panish {E}2{R} Text Adaptation",
    author = "Madina, Margot  and
      Gonzalez-Dios, Itziar  and
      Siegel, Melanie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.126",
    pages = "1422--1434",
    abstract = "The process of adapting and creating Easy-to-Read (E2R) texts is very expensive and time-consuming. Due to the success of Large Language Models (LLMs) such as ChatGPT and their ability to generate written language, it is likely to think that such models can help in the adaptation or creation of text in E2R. In this paper, we explore the concept of E2R, its underlying principles and applications, and provides a preliminary study on the usefulness of ChatGPT-4 for E2R text adaptation. We focus on the Spanish language and its E2R variant, Lectura F{\'a}cil (LF). We consider a range of prompts that can be used and the differences in output that this produces. We then carry out a three-folded evaluation on 10 texts adapted by ChatGPT-4: (1) an automated evaluation to check values related to the readability of texts, (2) a checklist-based manual evaluation (for which we also propose three new capabilities) and (3) a users{'} evaluation with people with cognitive disabilities. We show that it is difficult to choose the best prompt to make ChatGPT-4 adapt texts to LF. Furthermore, the generated output does not follow the E2R text rules, so it is often not suitable for the target audience.",
}

The process of adapting and creating Easy-to-Read (E2R) texts is very expensive and time-consuming. Due to the success of Large Language Models (LLMs) such as ChatGPT and their ability to generate written language, it is likely to think that such models can help in the adaptation or creation of text in E2R. In this paper, we explore the concept of E2R, its underlying principles and applications, and provides a preliminary study on the usefulness of ChatGPT-4 for E2R text adaptation. We focus on the Spanish language and its E2R variant, Lectura F{\'a}cil (LF). We consider a range of prompts that can be used and the differences in output that this produces. We then carry out a three-folded evaluation on 10 texts adapted by ChatGPT-4: (1) an automated evaluation to check values related to the readability of texts, (2) a checklist-based manual evaluation (for which we also propose three new capabilities) and (3) a users{'} evaluation with people with cognitive disabilities. We show that it is difficult to choose the best prompt to make ChatGPT-4 adapt texts to LF. Furthermore, the generated output does not follow the E2R text rules, so it is often not suitable for the target audience.
Madina, Margot Gonzalez-Dios, Itziar Siegel, Melanie
A Preliminary Study of ChatGPT for Spanish E2R Text Adaptation