Difficulty Estimation and Simplification of French Text Using LLMs
This work addresses language learning challenges by providing tools for text difficulty assessment and simplification, though it is incremental as it applies existing LLM methods to a specific domain.
The paper tackles the problem of estimating and simplifying the difficulty of French texts for language learning by using large language models, achieving superior accuracy in classification and meaningful simplifications with limited fine-tuning.
We leverage generative large language models for language learning applications, focusing on estimating the difficulty of foreign language texts and simplifying them to lower difficulty levels. We frame both tasks as prediction problems and develop a difficulty classification model using labeled examples, transfer learning, and large language models, demonstrating superior accuracy compared to previous approaches. For simplification, we evaluate the trade-off between simplification quality and meaning preservation, comparing zero-shot and fine-tuned performances of large language models. We show that meaningful text simplifications can be obtained with limited fine-tuning. Our experiments are conducted on French texts, but our methods are language-agnostic and directly applicable to other foreign languages.