Exploring Large Language Models to generate Easy to Read content
This research addresses text accessibility for people with cognitive impairments, but it is incremental as it applies existing LLM methods to a new domain and dataset.
This work tackled the problem of making text more accessible for individuals with cognitive impairments by exploring the use of Large Language Models (LLMs) to automatically simplify Spanish texts into Easy to Read formats, resulting in the creation of a parallel corpus and qualitative evaluation showing promising strategies.
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.