Gender bias in machine translation (MT) systems poses a significant challenge to achieving accurate and inclusive translations. This paper examines gender bias in machine translation systems for languages such as Telugu and Kannada from the Dravidian family, analyzing how gender inflections affect translation accuracy and neutrality using Google Translate and ChatGPT. It finds that while plural forms can reduce bias, individual-centric sentences often maintain the bias due to historical stereotypes. The study evaluates the Chain of Thought processing, noting significant bias mitigation from 80% to 4% in Telugu and from 40% to 0% in Kannada. It also compares Telugu and Kannada translations, emphasizing the need for language specific strategies to address these challenges and suggesting directions for future research to enhance fairness in both data preparation and prompts during inference.

研究审查了跟特拉如古语和卡纳达语这类属于达罗毗荼语系的机器翻译系统中的性别偏见带来的挑战，通过使用谷歌翻译和ChatGPT分析性别词尾对翻译准确性和中性的影响，发现复数形式可以减少偏见，但是个体中心的句子往往保持偏见，研究评估了智能链处理，在特拉如古语中性别偏见从80％减少到4％，在卡纳达语中从40％减少到0％，同时对比了特拉如古语和卡纳达语的翻译，强调了需要特定语言的策略来解决这些挑战，并提出了改善数据准备和推理过程中公平性的研究方向。

对于英德拉维底亚机器翻译中性别偏见缓解的思维链的重要性