{"ID":2861984,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00691","arxiv_id":"2510.00691","title":"Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments","abstract":"Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven ETR generation offers a scalable solution but faces key challenges, including dataset scarcity, domain adaptation, and balancing lightweight learning of Large Language Models (LLMs). In this paper, we introduce ETR-fr, the first dataset for ETR text generation fully compliant with European ETR guidelines. We implement parameter-efficient fine-tuning on PLMs and LLMs to establish generative baselines. To ensure high-quality and accessible outputs, we introduce an evaluation framework based on automatic metrics supplemented by human assessments. The latter is conducted using a 36-question evaluation form that is aligned with the guidelines. Overall results show that PLMs perform comparably to LLMs and adapt effectively to out-of-domain texts.","short_abstract":"Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven...","url_abs":"https://arxiv.org/abs/2510.00691","url_pdf":"https://arxiv.org/pdf/2510.00691v1","authors":"[\"François Ledoyen\",\"Gaël Dias\",\"Alexis Lechervy\",\"Jeremie Pantin\",\"Fabrice Maurel\",\"Youssef Chahir\",\"Elisa Gouzonnat\",\"Mélanie Berthelot\",\"Stanislas Moravac\",\"Armony Altinier\",\"Amy Khairalla\"]","published":"2025-10-01T09:13:18Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}