In this paper, we use large language models to generate personalized stories for language learners, using only the vocabulary they know. The generated texts are specifically written to teach the user new vocabulary by simply reading stories where it appears in context, while at the same time seamlessly reviewing recently learned vocabulary. The generated stories are enjoyable to read and the vocabulary reviewing/learning is optimized by a Spaced Repetition System. The experiments are conducted in three languages: English, Chinese and Polish, evaluating three story generation methods and three strategies for enforcing lexical constraints. The results show that the generated stories are more grammatical, coherent, and provide better examples of word usage than texts generated by the standard constrained beam search approach

翻译：本文利用大语言模型为语言学习者生成个性化故事，仅使用其已掌握的词汇。生成的文本专门设计为通过阅读包含新词汇的上下文故事来教授用户新词汇，同时无缝复习近期所学词汇。生成的故事具有可读趣味性，且词汇复习/学习过程通过间隔重复系统进行优化。实验在英语、汉语和波兰语三种语言中展开，评估了三种故事生成方法及三种词汇约束实施策略。结果表明，与标准约束束搜索方法生成的文本相比，本方法生成的故事在语法正确性、连贯性及词汇用法示例质量方面均表现更优。