Exploring Bengali Religious Dialect Biases in Large Language Models with Evaluation Perspectives

TL;DR

This paper investigates religious dialect biases in Bengali language models, analyzing how popular LLMs exhibit stereotypes related to Hindu and Muslim dialects, and discusses implications for fairness and ethical AI development.

Contribution

It provides a comparative analysis of biases in Bengali religious dialects across multiple LLMs and offers insights into evaluation perspectives for fairness in low-resource languages.

Findings

ChatGPT, Gemini, and Copilot show varying biases towards religious dialects.

Certain words trigger social biases more than others.

Analysis suggests specific linguistic features influence bias detection.

Abstract

While Large Language Models (LLM) have created a massive technological impact in the past decade, allowing for human-enabled applications, they can produce output that contains stereotypes and biases, especially when using low-resource languages. This can be of great ethical concern when dealing with sensitive topics such as religion. As a means toward making LLMS more fair, we explore bias from a religious perspective in Bengali, focusing specifically on two main religious dialects: Hindu and Muslim-majority dialects. Here, we perform different experiments and audit showing the comparative analysis of different sentences using three commonly used LLMs: ChatGPT, Gemini, and Microsoft Copilot, pertaining to the Hindu and Muslim dialects of specific words and showcasing which ones catch the social biases and which do not. Furthermore, we analyze our findings and relate them to potential reasons and evaluation perspectives, considering their global impact with over 300 million speakers worldwide. With this work, we hope to establish the rigor for creating more fairness in LLMs, as these are widely used as creative writing agents.