@inproceedings{gao-etal-2026-evaluating,
    title = "Evaluating Direct Preference Optimization for Personalizing {G}erman Automatic Text Simplifications for Persons with Intellectual Disabilities",
    author = {Gao, Yingqiang  and
      Johnson, Kaede  and
      Fr{\"o}hlich, David  and
      Carrer, Luisa  and
      Ebling, Sarah},
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.5/",
    pages = "43--62",
    ISBN = "979-8-89176-424-8",
    abstract = "Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in large language models (LLMs) have substantially improved the quality of machine-generated text simplifications, however, existing LLM-based ATS systems do not incorporate preference feedback during post-training, resulting in a lack of personalization tailored to the specific needs of target group persons. In this work, we propose an ATS personalization framework using direct preference optimization (DPO). Specifically, we post-trained LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences of paired text simplifications generated by mainstream LLMs. Our pipeline for developing personalized LLM-based ATS systems encompasses data collection, model selection, supervised fine-tuning (SFT) and DPO post-training, and result evaluation. Our findings underscore the necessity of active participation of target group persons in designing personalized inclusive AI solutions aligned with human preferences."
}