SRS-Stories: Vocabulary-constrained multilingual story generation for language learning
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Abstract (arXiv)
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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References · 8
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2018Neural Network Acceptability Judgmentscited by 952
- 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 451
- 2018Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translationcited by 181
- 2023GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4cited by 143
- 2016Unbounded Human Learning: Optimal Scheduling for Spaced Repetitioncited by 11
- 2025Exploring the Multilingual NLG Evaluation Abilities of LLM-Based Evaluatorscited by 5
- 2025OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMscited by 4
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