SectEval: Evaluating the Latent Sectarian Preferences of Large Language Models

TL;DR

This paper introduces SectEval, a benchmark to assess bias in large language models regarding sectarian views of Islam, revealing language and location-based biases in model responses.

Contribution

It presents the first systematic evaluation of sectarian bias in LLMs across languages and locations, highlighting inconsistencies and biases.

Findings

Models favor Shia in English and Sunni in Hindi.

Location influences model responses, with some models adapting answers based on country.

Biases vary significantly across models and languages.

Abstract

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/