用用户已知词汇生成多语言故事，帮学习者在阅读中自然学新词。
SRS-Stories: Vocabulary-constrained multilingual story generation for language learning
- 基于用户掌握词汇生成故事，自动控制生词出现
- 相比传统方法，语法更准、连贯性更强、用法示例更自然
- 适合语言学习者和教育科技开发者使用
本文利用大语言模型为语言学习者生成个性化故事，仅使用学习者已掌握的词汇。生成文本通过上下文自然呈现新词汇，同时无缝复习近期学过的词汇。故事设计结合了间隔重复系统（Spaced Repetition System），提升学习效率。实验在英语、中文和波兰语三种语言上进行，评估了三种故事生成方法和三种词汇约束策略。结果表明，生成的故事在语法正确性、连贯性和词汇使用示范方面均优于标准约束束搜索方法。
原文摘要 · Abstract (English)
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
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