  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.
