DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models

AbstractWhile 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.