This paper presents an analysis of biases in open-source Large Language Models (LLMs) across various genders, religions, and races. We introduce a methodology for generating a bias detection dataset using seven bias triggers: General Debate, Positioned Debate, Career Advice, Story Generation, Problem-Solving, Cover-Letter Writing, and CV Generation. We use GPT-4o to generate a diverse set of prompts for each trigger across various genders, religious and racial groups. We evaluate models from Llama and Gemma family on the generated dataset. We anonymise the LLM-generated text associated with each group using GPT-4o-mini and do a pairwise comparison using GPT-4o-as-a-Judge. To quantify bias in the LLM-generated text we use the number of wins and losses in the pairwise comparison. Our analysis spans three languages, English, German, and Arabic to explore how language influences bias manifestation. Our findings reveal that LLMs exhibit strong polarization toward certain groups across each category, with a notable consistency observed across models. However, when switching languages, variations and anomalies emerge, often attributable to cultural cues and contextual differences.

本研究分析了开源大型语言模型（LLMs）在性别、宗教和种族上的偏见，填补了现有研究在偏见检测方法上的空白。采用七种偏见触发器生成偏见检测数据集，并通过对比分析不同模型的产生的文本偏见，发现LLMs在不同群体间表现出强烈的极化现象，而语言的切换则引发了各种变异与异常，揭示了文化和语境对偏见表现的影响。

用一粒盐：大型语言模型在社会维度上的公平性研究