多语言模型也存在社会偏见，这篇综述系统梳理了跨语言偏见评估与缓解研究。
Social Bias in Multilingual Language Models: A Survey
- 系统回顾多语言场景下的偏见评估与缓解方法
- 揭示主流研究在语言选择和跨文化适配上的局限性
- 为未来更具包容性的多语言偏见研究指明方向
预训练多语言模型表现出与英语模型相同的社交偏见。本文系统性综述了将偏见评估与缓解方法拓展至多语言及非英语语境的新兴研究。我们从语言多样性、文化敏感性，以及评估指标与缓解技术的选择等方面分析相关文献。研究揭示了当前领域主流方法论设计中的不足（如对特定语言的偏好、多语言缓解实验稀缺），并整理了跨语言文化适配偏见基准时遇到的常见问题与已实施的解决方案。基于发现，本文提出未来研究方向，旨在增强多语言偏见研究的包容性、跨文化适宜性，并与前沿自然语言处理进展保持一致。
原文摘要 · Abstract (English)
Pretrained multilingual models exhibit the same social bias as models processing English texts. This systematic review analyzes emerging research that extends bias evaluation and mitigation approaches into multilingual and non-English contexts. We examine these studies with respect to linguistic diversity, cultural awareness, and their choice of evaluation metrics and mitigation techniques. Our survey illuminates gaps in the field's dominant methodological design choices (e.g., preference for certain languages, scarcity of multilingual mitigation experiments) while cataloging common issues encountered and solutions implemented in adapting bias benchmarks across languages and cultures. Drawing from the implications of our findings, we chart directions for future research that can reinforce the multilingual bias literature's inclusivity, cross-cultural appropriateness, and alignment with state-of-the-art NLP advancements.
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