用用户画像让大模型更懂不同人群对歧视语的感知差异
The Effects of Demographic Instructions on LLM Personas
- 基于用户人口统计信息，个性化调整大模型对歧视语的识别
- 保留多样标注而非统一标准，捕捉不同群体的主观感受
- 适合研究性别敏感内容检测或公平性优化的团队
社交媒体平台需根据政府法规过滤性别歧视内容。现有机器学习方法虽能基于标准化定义可靠检测歧视，但常忽视歧视语言的主观性，忽略个体用户视角。为弥补这一缺口，我们采用视角主义方法，保留多样化标注而非强制使用金标准标签或其聚合结果，使模型能够考虑个人或群体对歧视的特定认知。利用来自 Twitter 的人口统计数据，我们采用大语言模型（LLMs）实现歧视识别的个性化。
原文摘要 · Abstract (English)
Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism.
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