用数据复制让模型更贴近目标人群观点，无需额外标注。
Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication
- 通过复制少数群体标注数据，调整训练集分布。
- 修复了非代表性标注导致的模型校准偏差，准确率不变。
- 适合关注公平性与真实场景泛化的研究者。
基于众包标注训练的模型可能无法反映目标人群的真实观点，若标注者不具代表性。本文提出PAIR：人口对齐实例复制，一种无需新增标注的后处理方法，通过复制代表性不足标注者的样本，使训练数据更符合目标人群比例。在仇恨言论和攻击性语言检测的模拟实验中，我们发现非代表性标注池会严重损害模型校准能力，但对准确率影响较小。PAIR通过复制低代表群体的标注，有效修正了校准问题。论文最后提出提升训练数据代表性的实践建议。
原文摘要 · Abstract (English)
Models trained on crowdsourced annotations may not reflect population views, if those who work as annotators do not represent the broader population. In this paper, we propose PAIR: Population-Aligned Instance Replication, a post-processing method that adjusts training data to better reflect target population characteristics without collecting additional annotations. Using simulation studies on offensive language and hate speech detection with varying annotator compositions, we show that non-representative pools degrade model calibration while leaving accuracy largely unchanged. PAIR corrects these calibration problems by replicating annotations from underrepresented annotator groups to match population proportions. We conclude with recommendations for improving the representativity of training data and model performance.
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