  Gender-neutral translation (GNT) that avoids biased and undue binary
assumptions is a pivotal challenge for the creation of more inclusive
translation technologies. Advancements for this task in Machine Translation
(MT), however, are hindered by the lack of dedicated parallel data, which are
necessary to adapt MT systems to satisfy neutral constraints. For such a
scenario, large language models offer hitherto unforeseen possibilities, as
they come with the distinct advantage of being versatile in various (sub)tasks
when provided with explicit instructions. In this paper, we explore this
potential to automate GNT by comparing MT with the popular GPT-4 model. Through
extensive manual analyses, our study empirically reveals the inherent
limitations of current MT systems in generating GNTs and provides valuable
insights into the potential and challenges associated with prompting for
neutrality.
