34 citations · 51 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Design of an Open-Source Architecture for Neural Machine Translation
Séamus Lankford, Haithem Afli, Andy Way
adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is…
adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds
Séamus Lankford, Haithem Afli, Andy Way
The advent of Multilingual Language Models (MLLMs) and Large Language Models has spawned innovation in many areas of natural language processing. Despite the exciting potential of…