Large Language Models have been widely adopted in NLP tasks and applications, how- ever, their ability to accurately process Irish and other minority languages has not been fully explored. In this paper we describe prelim- inary experiments examining the capacity of publicly-available machine translation engines (Google Translate, Microsoft Bing, and eTrans- lation) and prompt-based AI systems systems (ChatGPT 3.5, Llama 2) for translating and handling challenging language features of Irish. A hand-crafted selection of challenging Irish language features were incorporated into trans- lation prompts, and the output from each model was examined by a human evaluator. The re- sults of these experiments indicate that these LLM-based models still struggle with translat- ing rare linguistic phenomena and ambiguous constructions. This preliminary analysis helps to inform further research in this field, pro- viding a simple ranking of publicly-available models, and indicating which language features require particular attention when evaluating model capacity.

Minority languages such as Irish are massively under-resourced, particularly in terms of high-quality domain-relevant data, limiting the capabilities of machine translation (MT) engines, even those integrating large language models (LLMs). The eSTÓR project, described in this paper, focuses on the collection and curation of high-quality Irish text data for diverse domains.