Language Models, Neural Networks, Transfer Learning, Vocabulary Swap.

We present a simple approach for efficiently adapting pre-trained English language models to generate text in lower-resource language, specifically Czech. We propose a vocabulary swap method that leverages parallel corpora to map tokens between languages, allowing the model to retain much of its learned capabilities. Experiments conducted on a Czech translation of the TinyStories dataset demonstrate that our approach significantly outperforms baseline methods, especially when using small amounts of training data. With only 10% of the data, our method achieves a perplexity of 17.89, compared to 34.19 for the next best baseline. We aim to contribute to work in the field of cross-lingual transfer in natural language processing and we propose a simple to implement, computationally efficient method tested in a controlled environment.