@inproceedings{bae-etal-2025-decap,
    title = "{D}e{CAP}: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models",
    author = "Bae, Suyoung  and
      Choi, YunSeok  and
      Lee, Jee-Hyong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/declare-journal/2025.naacl-long.624/",
    doi = "10.18653/v1/2025.naacl-long.624",
    pages = "12555--12574",
    ISBN = "979-8-89176-189-6",
    abstract = "While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but failto consider context and prevent bias propagation in the answers. To address this, we propose \textit{DeCAP}, a method for debiasing LLMs usingContext-Adaptive Prompt Generation. \textit{DeCAP} leverages a \textit{Question Ambiguity Detection} to take appropriate debiasing actions based on the context and a \textit{Neutral Answer Guidance Generation} to suppress the LLMs make objective judgments about the context, minimizing thepropagation of bias from their internal knowledge. Our various experiments across eight LLMs show that \textit{DeCAP} achieves state-of-the-art zero-shot debiased QA performance. This demonstrates \textit{DeCAP}{'}s efficacy in enhancing the fairness and accuracy of LLMs in diverseQA settings."
}