Machine translation (MT) models are known to suffer from gender bias, especially when translating into languages with extensive gendered morphology. Accordingly, they still fall short in using gender-inclusive language, also representative of non-binary identities. In this paper, we look at gender-inclusive neomorphemes, neologistic elements that avoid binary gender markings as an approach towards fairer MT. In this direction, we explore prompting techniques with large language models (LLMs) to translate from English into Italian using neomorphemes. So far, this area has been under-explored due to its novelty and the lack of publicly available evaluation resources. We fill this gap by releasing Neo-GATE, a resource designed to evaluate gender-inclusive en-it translation with neomorphemes. With Neo-GATE, we assess four LLMs of different families and sizes and different prompt formats, identifying strengths and weaknesses of each on this novel task for MT.

翻译：机器翻译（MT）模型存在性别偏见问题，尤其在翻译具有丰富性别形态的语言时表现显著。因此，这些模型仍难以使用性别包容性语言（亦涵盖非二元性别身份的表达）。本文聚焦性别包容性新词素——一类规避二元性别标记的新造语言元素——作为推进更公平机器翻译的解决方案。为此，我们探索利用大型语言模型（LLM）的提示技术，将英语通过新词素译为意大利语。这一领域因研究新颖且缺乏公开评估资源而长期未得到充分探索。我们通过发布Neo-GATE资源填补该空白，该资源专为评估基于新词素的性别包容性英-意翻译而设计。借助Neo-GATE，我们评估了四个不同系列与规模的大型语言模型，并测试了多种提示格式，从而明确了各模型在该机器翻译新任务中的优势与局限。