2025 COLING COLING 2025

Gaeilge Bhriste ó Shamhlacha Cliste: How Clever Are LLMs When Translating Irish Text?

Abstract

AbstractLarge 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.

The Questioner
🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Interdisciplinary and Natural Language Processing
🧭 Keyword Pioneer — prompt-based ai
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio