14 citations · 17 across the 3 of their papers we have counts for
3 papers
cs.CL2025★ 14 cited
Sociotechnical Effects of Machine Translation
Joss Moorkens, Andy Way, Séamus Lankford
While the previous chapters have shown how machine translation (MT) can be useful, in this chapter we discuss some of the side-effects and risks that are associated, and how they m…
cs.CL2024★ 1 cited
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages
Séamus Lankford, Andy Way
In an evolving landscape of crisis communication, the need for robust and adaptable Machine Translation (MT) systems is more pressing than ever, particularly for low-resource langu…
cs.CL2024★ 2 cited
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes
Inacio Vieira, Will Allred, Séamus Lankford +2
Decoder-only LLMs have shown impressive performance in MT due to their ability to learn from extensive datasets and generate high-quality translations. However, LLMs often struggle…