多语言大模型对伊斯兰教存在系统性偏见，跨语言表现差异明显。
Is Lying Only Sinful in Islam? Exploring Religious Bias in Multilingual Large Language Models Across Major Religions
- 构建南亚四大宗教语料库BRAND，涵盖2400+条中英双语数据
- 模型在英文下表现优于孟加拉语，且对伊斯兰教普遍存在偏见
- 揭示语言差异下宗教偏见的隐蔽性，适合关注伦理与AI公平的研究者
尽管大语言模型在偏差检测与分类方面取得进展，但宗教等敏感话题仍面临挑战，微小错误可能导致严重误解。特别是多语言模型常扭曲宗教内容，在宗教语境中准确性不足。为此，我们提出BRAND：双语宗教问责规范数据集，聚焦南亚四大宗教——佛教、基督教、印度教与伊斯兰教，包含超过2400条条目，并在英语和孟加拉语中使用三种不同提示进行测试。结果显示，模型在英语中的表现优于孟加拉语，且即使在宗教中立问题上也持续表现出对伊斯兰教的偏见。这些发现凸显了多语言模型在不同语言下存在的持久偏见。我们进一步将结果与人机交互领域关于宗教与灵性的更广泛问题联系起来。
原文摘要 · Abstract (English)
While recent developments in large language models have improved bias detection and classification, sensitive subjects like religion still present challenges because even minor errors can result in severe misunderstandings. In particular, multilingual models often misrepresent religions and have difficulties being accurate in religious contexts. To address this, we introduce BRAND: Bilingual Religious Accountable Norm Dataset, which focuses on the four main religions of South Asia: Buddhism, Christianity, Hinduism, and Islam, containing over 2,400 entries, and we used three different types of prompts in both English and Bengali. Our results indicate that models perform better in English than in Bengali and consistently display bias toward Islam, even when answering religion-neutral questions. These findings highlight persistent bias in multilingual models when similar questions are asked in different languages. We further connect our findings to the broader issues in HCI regarding religion and spirituality.
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