  Pre-trained large language models (LLM) are starting to be widely used in
many applications. In this work, we explore the use of these models in
interactive machine translation (IMT) environments. In particular, we have
chosen mBART (multilingual Bidirectional and Auto-Regressive Transformer) and
mT5 (multilingual Text-to-Text Transfer Transformer) as the LLMs to perform our
experiments. The system generates perfect translations interactively using the
feedback provided by the user at each iteration. The Neural Machine Translation
(NMT) model generates a preliminary hypothesis with the feedback, and the user
validates new correct segments and performs a word correction--repeating the
process until the sentence is correctly translated. We compared the performance
of mBART, mT5, and a state-of-the-art (SoTA) machine translation model on a
benchmark dataset regarding user effort, Word Stroke Ratio (WSR), Key Stroke
Ratio (KSR), and Mouse Action Ratio (MAR). The experimental results indicate
that mBART performed comparably with SoTA models, suggesting that it is a
viable option for this field of IMT. The implications of this finding extend to
the development of new machine translation models for interactive environments,
as it indicates that some novel pre-trained models exhibit SoTA performance in
this domain, highlighting the potential benefits of adapting these models to
specific needs.
