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.

Text simplification refers to the process of rewording within a single language, moving from a standard form into an easy-to-understand one. Easy Language and Plain Language are two examples of simplified varieties aimed at improving readability and understanding for a wide-ranging audience. Human evaluation of automatic text simplification is usually done by employing experts or crowdworkers to rate the generated texts. However, this approach does not include the target readers of simplified texts and does not reflect actual comprehensibility. In this paper, we explore different ways of measuring the quality of automatically simplified texts. We conducted a multi-faceted evaluation study involving end users, post-editors, and Easy Language experts and applied a variety of qualitative and quantitative methods. We found differences in the perception and actual comprehension of the texts by different user groups. In addition, qualitative surveys and behavioral observations proved to be essential in interpreting the results.