Using Large Language Models to Develop Readability Formulas for Educational Settings

Abstract

Readability formulas can be used to better match readers and texts. Current state-of-the-art readability formulas rely on large language models like transformer models (e.g., BERT) that model language semantics. However, the size and runtimes make them impractical in educational settings. This study examines the effectiveness of new readability formulas developed on the CommonLit Ease of Readability (CLEAR) corpus using more efficient sentence-embedding models including doc2vec, Universal Sentence Encoder, and Sentence BERT. This study compares sentence-embedding models to traditional readability formulas, newer NLP-informed linguistic feature formulas, and newer BERT-based models. The results indicate that sentence-embedding readability formulas perform well and are practical for use in various educational settings. The study also introduces an open-source NLP website to readily assess the readability of texts along with an application programming interface (API) that can be integrated into online educational learning systems to better match texts to readers.