Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning
This work addresses the challenge of maintaining learner engagement in language acquisition by providing personalized content, though it is incremental as it applies existing LLMs to a specific domain.
The paper tackles the problem of matching foreign language learners with appropriate content by using large language models to estimate linguistic difficulty and discover topic-relevant materials, resulting in a more precise difficulty estimation than traditional readability measures and offering both text and video content.
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.