@inproceedings{lamb-etal-2025-synthesising,
    title = "Synthesising a Corpus of {G}aelic Traditional Narrative with Cross-Lingual Text Expansion",
    author = "Lamb, William  and
      Han, Dongge  and
      Klejch, Ondrej  and
      Alex, Beatrice  and
      Bell, Peter",
    editor = "Davis, Brian  and
      Fransen, Theodorus  and
      Dhonnchadha, Elaine U{\'i}  and
      Walsh, Abigail",
    booktitle = "Proceedings of the 5th Celtic Language Technology Workshop",
    month = jan,
    year = "2025",
    address = "Abu Dhabi [Virtual Workshop]",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.cltw-1.2/",
    pages = "12--26",
    abstract = "Advances in large language modelling have disproportionately benefited high-resource languages due to their vastly greater training data reserves. This paper proposes a novel cross-lingual text expansion (XLTE) technique using multilingual large language models (MLLMs) to mitigate data sparsity in low-resource languages. We apply XLTE to the domain of traditional Scottish Gaelic storytelling to generate a training corpus suitable for language modelling, for example as part of an automatic speech recognition system. The effectiveness of this technique is demonstrated using OpenAI`s GPT-4o, with supervised fine-tuning (SFT) providing decreased neologism rates and a 57.2{\%} reduction in perplexity over the baseline model. Despite these promising results, qualitative analyses reveal important stylistic divergences between synthesised and genuine data. Nevertheless, XLTE offers a promising, scalable method for synthesising training sets in other languages and domains, opening avenues for further improvements in low-resource language modelling."
}