3 citations · 4 across the 3 of their papers we have counts for
9 papers
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…
adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translation
Séamus Lankford, Haithem Afli, Andy Way
adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for bo…
Human Evaluation of English--Irish Transformer-Based NMT
Séamus Lankford, Haithem Afli, Andy Way
In this study, a human evaluation is carried out on how hyperparameter settings impact the quality of Transformer-based Neural Machine Translation (NMT) for the low-resourced Engli…
Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021
Séamus Lankford, Haithem Afli, Andy Way
Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Co…