In machine translation, the problem of ambiguously gendered input has been pointed out, where the gender of an entity is not available in the source sentence. To address this ambiguity issue, the task of controlled translation that takes the gender of the ambiguous entity as additional input have been proposed. However, most existing works have only considered a simplified setup of one target gender for input. In this paper, we tackle controlled translation in a more realistic setting of inputs with multiple entities and propose Gender-of-Entity (GoE) prompting method for LLMs. Our proposed method instructs the model with fine-grained entity-level gender information to translate with correct gender inflections. By utilizing four evaluation benchmarks, we investigate the controlled translation capability of LLMs in multiple dimensions and find that LLMs reach state-of-the-art performance in controlled translation. Furthermore, we discover an emergence of gender interference phenomenon when controlling the gender of multiple entities. Finally, we address the limitations of existing gender accuracy evaluation metrics and propose leveraging LLMs as an evaluator for gender inflection in machine translation.

本研究针对机器翻译中模糊性别输入的问题，提出了性别控制翻译的方法，以多个实体为输入进行更加真实的控制翻译。研究发现，使用性别实体（GoE）提示法后，大型语言模型能够以细粒度的性别信息进行准确翻译，并在四个评估基准上表现出最佳效果，同时揭示了多实体性别干扰现象。

基于大型语言模型的细粒度性别控制机器翻译