Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals' lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective. While the existence of gender bias in language models has been acknowledged in previous studies, there remains a lack of consensus on how to effectively measure and evaluate this bias. Our survey critically examines the existing literature on gender bias in Transformers, shedding light on the diverse methodologies and metrics employed to assess bias. Several limitations in current approaches to measuring gender bias in Transformers are identified, encompassing the utilization of incomplete or flawed metrics, inadequate dataset sizes, and a dearth of standardization in evaluation methods. Furthermore, our survey delves into the potential ramifications of gender bias in Transformers for downstream applications, including dialogue systems and machine translation. We underscore the importance of fostering equity and fairness in these systems by emphasizing the need for heightened awareness and accountability in developing and deploying language technologies. This paper serves as a comprehensive overview of gender bias in Transformer models, providing novel insights and offering valuable directions for future research in this critical domain.

翻译：人工智能（AI）中的性别偏见已成为一个紧迫问题，对个人的生活具有深远影响。本文从语言学视角出发，对Transformer模型中的性别偏见进行了全面综述。尽管先前的研究已承认语言模型中存在性别偏见，但如何有效测量和评估这种偏见仍未达成共识。我们的综述批判性地审视了Transformer性别偏见的现有文献，揭示了用于评估偏见的多样化方法论和指标体系。本文识别了当前Transformer性别偏见测量方法中的若干局限，包括使用不完整或有缺陷的指标、数据集规模不足以及评估方法缺乏标准化。此外，我们的综述深入探讨了Transformer中性别偏见对下游应用（包括对话系统和机器翻译）的潜在影响。我们强调在开发和部署语言技术时，需增强意识与责任担当，以促进这些系统中的公平公正。本文作为Transformer模型性别偏见的综合性概述，为该关键领域提供了新颖见解，并为未来研究指明了有价值的方向。