Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning

TL;DR

This paper presents a method using large language models to estimate the difficulty of foreign language content, specifically French, to personalize and improve language learning experiences through topic relevance and content complexity assessment.

Contribution

It introduces a novel approach leveraging large language models for precise difficulty estimation and content selection, adaptable to various languages and content types including videos.

Findings

Enhanced content difficulty estimation over traditional readability measures

Increased learner engagement through personalized content selection

Applicability to both textual and video content

Abstract

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.