Large Language Models for Difficulty Estimation of Foreign Language
  Content with Application to Language Learning
Michalis Vlachos Mircea Lungu Yash Raj Shrestha Johannes-Rudolf David
We use large language models to aid learners enhance proficiency in a foreign
language. This is accomplished by identifying content on topics that the user
is interested in, and that closely align with the learner's proficiency level
in that foreign language. Our work centers on French content, but our approach
is readily transferable to other languages. Our solution offers several
distinctive characteristics that differentiate it from existing
language-learning solutions, such as, a) the discovery of content across topics
that the learner cares about, thus increasing motivation, b) a more precise
estimation of the linguistic difficulty of the content than traditional
readability measures, and c) the availability of both textual and video-based
content. The linguistic complexity of video content is derived from the video
captions. It is our aspiration that such technology will enable learners to
remain engaged in the language-learning process by continuously adapting the
topics and the difficulty of the content to align with the learners' evolving
interests and learning objectives.