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    <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.</abstract>
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%0 Conference Proceedings
%T Synthesising a Corpus of Gaelic Traditional Narrative with Cross-Lingual Text Expansion
%A Lamb, William
%A Han, Dongge
%A Klejch, Ondrej
%A Alex, Beatrice
%A Bell, Peter
%Y Davis, Brian
%Y Fransen, Theodorus
%Y Dhonnchadha, Elaine Ui
%Y Walsh, Abigail
%S Proceedings of the 5th Celtic Language Technology Workshop
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi [Virtual Workshop]
%F lamb-etal-2025-synthesising
%X 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.
%U https://aclanthology.org/2025.cltw-1.2/
%P 12-26

Markdown (Informal)

[Synthesising a Corpus of Gaelic Traditional Narrative with Cross-Lingual Text Expansion](https://aclanthology.org/2025.cltw-1.2/) (Lamb et al., CLTW 2025)

• Synthesising a Corpus of Gaelic Traditional Narrative with Cross-Lingual Text Expansion (Lamb et al., CLTW 2025)

ACL

• William Lamb, Dongge Han, Ondrej Klejch, Beatrice Alex, and Peter Bell. 2025. Synthesising a Corpus of Gaelic Traditional Narrative with Cross-Lingual Text Expansion. In Proceedings of the 5th Celtic Language Technology Workshop, pages 12–26, Abu Dhabi [Virtual Workshop]. International Committee on Computational Linguistics.