A Prompt Response to the Demand for Automatic Gender-Neutral Translation
This addresses the challenge of creating more inclusive translation technologies for users affected by gender bias, but it is incremental as it focuses on analyzing existing methods rather than proposing a new solution.
The study tackled the problem of gender-neutral translation in machine translation by comparing MT systems with GPT-4, revealing limitations in current MT systems and providing insights into prompting for neutrality.
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.