@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://aclanthology.org/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 *DeCAP*, a method for debiasing LLMs usingContext-Adaptive Prompt Generation. *DeCAP* leverages a *Question Ambiguity Detection* to take appropriate debiasing actions based on the context and a *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 *DeCAP* achieves state-of-the-art zero-shot debiased QA performance. This demonstrates *DeCAP*{'}s efficacy in enhancing the fairness and accuracy of LLMs in diverseQA settings."
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<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 *DeCAP*, a method for debiasing LLMs usingContext-Adaptive Prompt Generation. *DeCAP* leverages a *Question Ambiguity Detection* to take appropriate debiasing actions based on the context and a *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 *DeCAP* achieves state-of-the-art zero-shot debiased QA performance. This demonstrates *DeCAP*’s efficacy in enhancing the fairness and accuracy of LLMs in diverseQA settings.</abstract>
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%0 Conference Proceedings
%T DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models
%A Bae, Suyoung
%A Choi, YunSeok
%A Lee, Jee-Hyong
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F bae-etal-2025-decap
%X 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 *DeCAP*, a method for debiasing LLMs usingContext-Adaptive Prompt Generation. *DeCAP* leverages a *Question Ambiguity Detection* to take appropriate debiasing actions based on the context and a *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 *DeCAP* achieves state-of-the-art zero-shot debiased QA performance. This demonstrates *DeCAP*’s efficacy in enhancing the fairness and accuracy of LLMs in diverseQA settings.
%R 10.18653/v1/2025.naacl-long.624
%U https://aclanthology.org/2025.naacl-long.624/
%U https://doi.org/10.18653/v1/2025.naacl-long.624
%P 12555-12574
Markdown (Informal)
[DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models](https://aclanthology.org/2025.naacl-long.624/) (Bae et al., NAACL 2025)
ACL