Decoder-only Large Language Models (LLMs) have demonstrated potential in machine translation (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 harness this flexibility to explore LLaMa's capability to produce gender-specific translations for languages with grammatical gender. Our results indicate that LLaMa can generate gender-specific translations with competitive accuracy and gender bias mitigation when compared to NLLB, a state-of-the-art multilingual NMT system. Furthermore, our experiments reveal that LLaMa's translations are robust, showing significant performance drops when evaluated against opposite-gender references in gender-ambiguous datasets but maintaining consistency in less ambiguous contexts. This research provides insights into the potential and challenges of using LLMs for gender-specific translations and highlights the importance of in-context learning to elicit new tasks in LLMs.

翻译：仅解码器的大语言模型（LLMs）在机器翻译（MT）中已展现出潜力，尽管其性能略逊于传统的编码器-解码器神经机器翻译（NMT）系统。然而，大语言模型具备一项独特优势：可通过提示词控制输出属性。在本研究中，我们利用这一灵活性探索LLaMa在具有语法性别的语言中生成性别特定翻译的能力。结果表明，与先进的多语言神经机器翻译系统NLLB相比，LLaMa能以相近的准确率生成性别特定翻译，并有效缓解性别偏见。此外，实验发现LLaMa的翻译具有鲁棒性：在性别模糊数据集中，当以相反性别参考进行评估时，其性能显著下降；但在歧义较少的上下文中则保持一致性。本研究揭示了利用大语言模型进行性别特定翻译的潜力与挑战，并强调了通过上下文学习激发大语言模型新任务能力的重要性。