@inproceedings{eckman-etal-2025-aligning,
    title = "Aligning {NLP} Models with Target Population Perspectives using {PAIR}: Population-Aligned Instance Replication",
    author = "Eckman, Stephanie  and
      Ma, Bolei  and
      Kern, Christoph  and
      Chew, Rob  and
      Plank, Barbara  and
      Kreuter, Frauke",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Frenda, Simona  and
      Tonelli, Sara  and
      Dudy, Shiran",
    booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nlperspectives-1.9/",
    pages = "100--110",
    ISBN = "979-8-89176-350-0",
    abstract = "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."
}