Papers by Wiktor Kamzela

2 papers
SRS-Stories: Vocabulary-constrained multilingual story generation for language learning (2025.emnlp-industry)

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Challenge: Existing methods for learning foreign languages are to use a spaced repetition system to learn new vocabulary.
Approach: They use large language models to generate personalized stories using only the vocabulary they know.
Outcome: The generated stories are more grammatical, coherent, and provide better examples of word usage than the standard beam search approach.
How Important is ‘Perfect’ English for Machine Translation Prompts? (2026.findings-eacl)

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Challenge: Large language models (LLMs) are largely trained on and respond best to English prompts, but are also sensitive to errors in user prompts.
Approach: They propose to model a range of error types exhibited by second language English speakers and quantify their impact on LLM performance.
Outcome: The proposed model is brittle to natural spelling errors but not to errors at the phrasal level, but the variance in quality caused by these errors is lower than the variance over the initial prompt choice.

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