@inproceedings{kuoraite-gruzauskas-2025-simplifying,
    title = "Simplifying {L}ithuanian text into Easy-to-Read language using large language models",
    author = "Kuorait{\.{e}}, Simona  and
      Gru{\v{z}}auskas, Valentas",
    editor = "Ginel, Mar{\'i}a Isabel Rivas  and
      Cadwell, Patrick  and
      Canavese, Paolo  and
      Hansen-Schirra, Silvia  and
      Kappus, Martin  and
      Matamala, Anna  and
      Noonan, Will",
    booktitle = "Proceedings of the 1st Workshop on Artificial Intelligence and Easy and Plain Language in Institutional Contexts (AI {\&} EL/PL)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aielpl-1.4/",
    pages = "30--37",
    ISBN = "978-2-9701897-5-6",
    abstract = "This paper explores the task of simplifying Lithuanian text into Easy-to-Read language. Easy-to-Read language is a text written in short, clear sentences and simple words, adapted for people with intellectual disabilities or limited language skills. The aim of this work is to investigate how the large language model Lt-Llama-2-7b-hf, pre-trained on Lithuanian language data, can be adapted to the task of simplifying Lithuanian texts into Easy-to-Read language. To achieve this goal, specialized datasets were developed to fine-tune the model, and experiments were carried out. The model was tested by presenting the texts in their original language and the texts with a prompt adapted to the task. The results were evaluated using the SARI metric for assessing the quality of simplified texts and a qualitative evaluation of the large language model. The results show that the fine-tuned model sometimes simplifies text better than a not fine-tuned model, but that a larger and more extensive dataset would be needed to achieve significant results, and that more research should be carried out on fine-tuning the model for this task."
}