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.

翻译：性别中立翻译（GNT）旨在避免带有偏见的、不恰当的二元假设，这是构建更具包容性翻译技术的关键挑战。然而，机器翻译（MT）在该任务上的进展受到专用平行语料库匮乏的制约——这些数据对于使MT系统满足中立约束至关重要。在此背景下，大语言模型提供了前所未有的可能性，其独特优势在于：当提供明确指令时，能够在多种（子）任务中展现出卓越的通用性。本文通过对比MT系统与主流GPT-4模型，探索了自动化性别中立翻译的潜力。基于大量人工分析，本研究的实证结果揭示了当前MT系统在生成GNT时的固有局限性，并为通过提示工程实现中立性的潜力与挑战提供了宝贵见解。