Prompt Engineering Enhances Faroese MT, but Only Humans Can Tell

Barbara Scalvini, Annika Simonsen, Iben Nyholm Debess, Hafsteinn Einarsson

Abstract: This study evaluates GPT-4’s English-to-Faroese translation capabilities, comparing it with multilingual models on FLORES-200 and Sprotin datasets. We propose a prompt optimization strategy using Semantic Textual Similarity (STS) to improve translation quality. Human evaluation confirms the effectiveness of STS-based few-shot example selection, though automated metrics fail to capture these improvements. Our findings advance LLM applications for low-resource language translation while highlighting the need for better evaluation methods in this context.

External IDs:dblp:conf/nodalida/ScalviniSDE25