The development of Large Language Models (LLMs) has predominantly focused on
high-resource languages, leaving extremely low-resource languages like Irish
with limited representation. This work presents UCCIX, a pioneering effort on
the development of an open-source Irish-based LLM. We propose a novel framework
for continued pre-training of LLMs specifically adapted for extremely
low-resource languages, requiring only a fraction of the textual data typically
needed for training LLMs according to scaling laws. Our model, based on Llama
2-13B, outperforms much larger models on Irish language tasks with up to 12%
performance improvement, showcasing the effectiveness and efficiency of our
approach. We also contribute comprehensive Irish benchmarking datasets,
including IrishQA, a question-answering dataset, and Irish version of MT-bench.
These datasets enable rigorous evaluation and facilitate future research in
Irish LLM systems. Our work aims to preserve and promote the Irish language,
knowledge, and culture of Ireland in the digital era while providing a
framework for adapting LLMs to other indigenous languages.

该研究通过提出一种新的框架，使用极少量的文本数据来进行大规模语言模型的训练，开发了 UCCIX，一个面向爱尔兰语的开源语言模型，并在爱尔兰语任务中取得了高达 12% 的性能提升，提供了爱尔兰语数据集以便进行评估和未来研究，旨在推动爱尔兰语在数字时代的发展和其它土著语言的应用。

UCCIX: 爱尔兰卓越大型语言模型

UCCIX: Irish-eXcellence Large Language Model

ASR systems are generally built for the spoken 'standard', and their
performance declines for non-standard dialects/varieties. This is a problem for
a language like Irish, where there is no single spoken standard, but rather
three major dialects: Ulster (Ul), Connacht (Co) and Munster (Mu). As a
diagnostic to quantify the effect of the speaker's dialect on recognition
performance, 12 ASR systems were trained, firstly using baseline
dialect-balanced training corpora, and then using modified versions of the
baseline corpora, where dialect-specific materials were either subtracted or
added. Results indicate that dialect-balanced corpora do not yield a similar
performance across the dialects: the Ul dialect consistently underperforms,
whereas Mu yields lowest WERs. There is a close relationship between Co and Mu
dialects, but one that is not symmetrical. These results will guide future
corpus collection and system building strategies to optimise for cross-dialect
performance equity.

研究了影响语音自动识别（ASR）系统跨音系瓶颈的爱尔兰三个主要方言的对比分析，结果显示平衡方言的语料库无法在方言之间产生相似的表现，这些结果将指导未来语料收集和系统构建策略以优化跨方言表现公正性。