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.

使用解码器-only 大型语言模型（LLMs）进行性别特定翻译研究，发现通过提示可以控制输出属性，LLMs比传统编码器-解码器神经机器翻译系统（NMT）提供独特优势，能够生成性别特定的翻译且具有竞争性准确性和性别偏见缓解，同时揭示了LLMs在性别模糊数据集中表现出明显性能下降但在上下文较少歧义的情况下保持一致的鲁棒性，并突出了上下文学习对于在LLMs中引发新任务的重要性。

大型语言模型中的性别特定机器翻译