Evaluation and simplification of text difficulty using LLMs in the context of recommending texts in French to facilitate language learning

Henri Jamet, Maxime Manderlier, Yash Raj Shrestha, Michalis Vlachos

Abstract: Learning a new language can be challenging. To help learners, we built a recommendation system that suggests texts and videos based on the learners’ skill level of the language and topic interests. Our system analyzes content to determine its difficulty and topic, and, if needed, can simplify complex texts while maintaining semantics. Our work explores the holistic use of Large Language Models (LLMs) for the various sub-tasks involved for accurate recommendations: difficulty estimation and simplification, graph recommender engine, topic estimation. We present a comprehensive evaluation comparing zero-shot and fine-tuned LLMs, demonstrating significant improvements in French content difficulty prediction (18-56%), topic prediction accuracy (27%), and recommendation relevance (up to 18% NDCG increase).