  While machine translation (MT) systems have seen significant improvements, it
is still common for translations to reflect societal biases, such as gender
bias. Decoder-only Large Language Models (LLMs) have demonstrated potential in
MT, albeit with performance slightly lagging behind traditional encoder-decoder
Neural Machine Translation (NMT) systems. However, LLMs offer a unique
advantage: the ability to control the properties of the output through prompts.
In this study, we leverage this flexibility to explore LLaMa's capability to
produce gender-specific translations. Our results indicate that LLaMa can
generate gender-specific translations with translation accuracy and gender bias
comparable to NLLB, a state-of-the-art multilingual NMT system. Furthermore,
our experiments reveal that LLaMa's gender-specific translations rely on
coreference resolution to determine gender, showing higher gender variance in
gender-ambiguous datasets but maintaining consistency in less ambiguous
contexts. This research investigates the potential and challenges of using LLMs
for gender-specific translations as an instance of the controllability of
outputs offered by LLMs.
