SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models
As Large Language Models (LLMs) becomes a popular source for religious knowledge, it is important to know if it treats different groups fairly. This study is the first to measure how LLMs handle the differences between the two main sects of Islam: Sunni and Shia. We present a test called SectEval, available in both English and Hindi, consisting of 88 questions, to check the bias-ness of 15 top LLM models, both proprietary and open-weights. Our results show a major inconsistency based on language. In English, many powerful models DeepSeek-v3 and GPT-4o often favored Shia answers. However, when asked the exact same questions in Hindi, these models switched to favoring Sunni answers. This me...
| Authors | Aditya Maheshwari, Amit Gajkeshwar, Kaushal Sharma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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