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.

翻译：我们利用大语言模型帮助学习者提升外语水平。具体而言，通过识别用户感兴趣主题且与该学习者外语熟练度高度匹配的内容来实现这一目标。本研究以法语内容为核心，但该方法可便捷迁移至其他语言。我们的解决方案具有若干显著特征，使其区别于现有语言学习方案，包括：a) 发现学习者在意的多主题内容，从而增强学习动机；b) 相较于传统可读性指标，能更精确地评估内容的语言难度；c) 支持文本与视频两种内容类型。视频内容的语言复杂度通过字幕推导得出。我们期望此类技术能够通过持续调整内容主题与难度，使其与学习者不断变化的兴趣及学习目标保持同步，从而维持学习者在语言学习过程中的参与度。