有时模型确在说教：通过亚洲国家人口统计分析量化开放大语言模型中的宗教偏见
Sometimes the Model doth Preach: Quantifying Religious Bias in Open LLMs through Demographic Analysis in Asian Nations
AI总结：
本文提出基于汉明距离量化开放大语言模型社会人口特征的方法，在亚洲多国宗教宽容与身份认同调查上评估 Llama 和 Mistral，发现模型普遍呈现单一同质化画像，存在推广霸权世界观并削弱少数群体视角的风险。
摘要：
大语言模型（LLMs）能够生成观点并无意中传播偏见，这些偏见源于不具代表性和缺乏多样性的数据收集。先前的研究主要针对西方（尤其是美国）分析了这些观点，但由此得出的见解可能无法推广到非西方人群。随着来自各行各业用户广泛使用大语言模型系统，每个生成输出的文化敏感性变得至关重要。我们的工作提出了一种新方法，用于定量分析大语言模型生成的观点，在提取模型的社会人口特征方面改进了先前的工作。我们的方法通过汉明距离（Hamming Distance）测量大语言模型回答与调查受访者之间的距离，以推断模型输出中所反映的人口统计特征。我们在多个全球南方国家开展的调查上评估了现代开放大语言模型，如 Llama 和 Mistral，重点关注印度和其他亚洲国家，具体评估模型在与宗教宽容和身份认同相关的调查上的表现。我们的分析揭示，大多数开放大语言模型匹配单一的同质化画像，且在不同国家/地区之间有所变化，这反过来引发了关于大语言模型推广霸权主义世界观并削弱不同少数群体视角的风险的疑问。我们的框架也可能有助于未来研究调查训练数据、模型架构与由此在大语言模型输出中反映的偏见之间的复杂交叉关系，尤其是在宗教宽容和身份认同等敏感话题方面。
英文摘要：
Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
