  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.
