SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models
This reveals that LLMs provide inconsistent religious advice based on language and location, highlighting fairness issues for users seeking religious knowledge.
This study introduced SectEval, a test with 88 questions in English and Hindi, to evaluate bias in 15 large language models regarding Sunni and Shia Islam. Results showed major inconsistencies: models like DeepSeek-v3 and GPT-4o favored Shia in English but Sunni in Hindi, and advanced models like Claude-3.5 adjusted answers based on user location.
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 means a user could get completely different religious advice just by changing languages. We also looked at how models react to location. Advanced models Claude-3.5 changed their answers to match the user's country-giving Shia answers to a user from Iran and Sunni answers to a user from Saudi Arabia. In contrast, smaller models (especially in Hindi) ignored the user's location and stuck to a Sunni viewpoint. These findings show that AI is not neutral; its religious ``truth'' changes depending on the language you speak and the country you claim to be from. The data set is available at https://github.com/secteval/SectEval/