Ensuring text accessibility and understandability are essential goals, particularly for individuals with cognitive impairments and intellectual disabilities, who encounter challenges in accessing information across various mediums such as web pages, newspapers, administrative tasks, or health documents. Initiatives like Easy to Read and Plain Language guidelines aim to simplify complex texts; however, standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to systematically simplify Spanish texts into Easy to Read formats, with a focus on utilizing Large Language Models (LLMs) for simplifying texts, especially in generating Easy to Read content. The study contributes a parallel corpus of Spanish adapted for Easy To Read format, which serves as a valuable resource for training and testing text simplification systems. Additionally, several text simplification experiments using LLMs and the collected corpus are conducted, involving fine-tuning and testing a Llama2 model to generate Easy to Read content. A qualitative evaluation, guided by an expert in text adaptation for Easy to Read content, is carried out to assess the automatically simplified texts. This research contributes to advancing text accessibility for individuals with cognitive impairments, highlighting promising strategies for leveraging LLMs while responsibly managing energy usage.

翻译：确保文本的可访问性和可理解性是至关重要的目标，尤其对于认知障碍和智力残疾人群而言，他们在获取网页、报纸、行政事务或健康文件等多种媒介信息时面临挑战。诸如"易读"和"简明语言"指南等倡议旨在简化复杂文本，然而标准化这些指南仍然具有挑战性，且通常涉及人工处理。本研究对利用人工智能和自然语言处理方法系统地将西班牙语文本简化为易读格式进行了探索性调查，重点关注使用大语言模型进行文本简化，特别是在生成易读内容方面的应用。本研究贡献了一个适用于易读格式的西班牙语平行语料库，为训练和测试文本简化系统提供了宝贵资源。此外，研究利用大语言模型和所收集的语料库进行了多项文本简化实验，包括微调和测试Llama2模型以生成易读内容。在易读内容文本适配专家的指导下，对自动简化文本进行了定性评估。这项研究有助于推进认知障碍人群的文本可访问性，同时为合理利用大语言模型并负责任地管理能源消耗提供了有前景的策略。