用于缓解大语言模型中针对阿拉伯人和穆斯林的文化偏见的提示工程技术：一项系统综述
Prompt Engineering Techniques for Mitigating Cultural Bias Against Arabs and Muslims in Large Language Models: A Systematic Review
AI总结：
本系统综述遵循PRISMA与Kitchenham方法，分析8项2021-2024年的实证研究，梳理5类缓解大模型针对阿拉伯人和穆斯林文化偏见的提示工程方法，验证其效果差异并指出未来研究方向。
摘要：
大语言模型已在多个领域展现出卓越能力，但针对其文化偏见——尤其是针对阿拉伯人和穆斯林的偏见——的担忧带来了重大伦理挑战，这类偏见会持续传播有害刻板印象并加剧边缘化。尽管人们日益认识到大语言模型中存在偏见，但专门针对阿拉伯人和穆斯林群体表征的提示工程策略仍未得到充分研究。本项混合方法系统综述对这类技术展开研究，为研究人员和从业者提供基于实证的指导。我们遵循PRISMA指南与Kitchenham系统综述方法，分析了2021至2024年间发表的8项探究偏见缓解策略的实证研究。研究结果揭示了五种主要的提示工程方法：文化提示、情感启动、自我去偏技术、结构化多步流水线，以及参数优化的连续提示。尽管所有方法都展现出减少偏见的潜力，但不同研究、不同偏见类型下的效果差异极大。有证据表明，某些偏见类型可能比其他类型更难通过基于提示的方法缓解。结构化多步流水线整体效果最佳，偏见减少幅度最高可达87.7%，但这类方法要求使用者具备更高的技术专业能力。文化提示则适用范围更广，同时也具备不错的效果。这些结果凸显了提示工程在无需访问模型参数的情况下缓解文化偏见的可行性。本次纳入的研究数量有限，表明这一关键领域存在显著的研究空白。未来研究应聚焦于开发文化自适应提示技术、创建针对阿拉伯人和穆斯林的评估资源，以及将提示工程与互补的去偏方法相结合，在维持模型效用的同时解决更深层次的刻板印象问题。
英文摘要：
Large language models have demonstrated remarkable capabilities across various domains, yet concerns about cultural bias - particularly towards Arabs and Muslims - pose significant ethical challenges by perpetuating harmful stereotypes and marginalization. Despite growing recognition of bias in LLMs, prompt engineering strategies specifically addressing Arab and Muslim representation remain understudied. This mixed-methods systematic review examines such techniques, offering evidence-based guidance for researchers and practitioners. Following PRISMA guidelines and Kitchenham's systematic review methodology, we analyzed 8 empirical studies published between 2021-2024 investigating bias mitigation strategies. Our findings reveal five primary prompt engineering approaches: cultural prompting, affective priming, self-debiasing techniques, structured multi-step pipelines, and parameter-optimized continuous prompts. Although all approaches show potential for reducing bias, effectiveness varied substantially across studies and bias types. Evidence suggests that certain bias types may be more resistant to prompt-based mitigation than others. Structured multi-step pipelines demonstrated the highest overall effectiveness, achieving up to 87.7% reduction in bias, though they require greater technical expertise. Cultural prompting offers broader accessibility with substantial effectiveness. These results underscore the accessibility of prompt engineering for mitigating cultural bias without requiring access to model parameters. The limited number of studies identified highlights a significant research gap in this critical area. Future research should focus on developing culturally adaptive prompting techniques, creating Arab and Muslim-specific evaluation resources, and integrating prompt engineering with complementary debiasing methods to address deeper stereotypes while maintaining model utility.
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