@inproceedings{fonseca-etal-2025-instance,
    title = "Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification",
    author = "Fonseca, Guilherme  and
      Cunha, Washington  and
      Prenassi, Gabriel  and
      Gon{\c{c}}alves, Marcos Andr{\'e}  and
      Rocha, Leonardo Chaves Dutra Da",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.458/",
    pages = "9323--9340",
    ISBN = "979-8-89176-251-0",
    abstract = "Skewness in imbalanced datasets affects Automatic Text Classification (ATC), leading to classifier bias toward the majority classes. This work examines undersampling methods to mitigate such bias in Small and Large Language Model (SLMs and LLMs) classifiers. Based on the limitations found in existing solutions, we propose two novel undersampling methods inspired by state-of-the-art Instance Selection techniques, relying on calibrated confidences and semantic difficulty estimates. We compare them against 19 baselines across 13 datasets, evaluating: (i) effectiveness, (ii) class imbalance bias, (iii) efficiency, (iv) scalability, and (v) consistency. Results show our methods uniquely reduce classifier bias (up to 56{\%}) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50{\%})."
}