Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias
This work addresses the lack of bias assessment in Bangla NLP, which is crucial for ensuring fair use in sensitive fields, though it is incremental as it extends existing bias studies from English to a new language.
The study tackled the problem of social bias in large language models for the Bangla language, focusing on gender and religious biases, and provided a curated dataset and testing of probing techniques for bias detection, with all resources made publicly available.
The rapid growth of Large Language Models (LLMs) has put forward the study of biases as a crucial field. It is important to assess the influence of different types of biases embedded in LLMs to ensure fair use in sensitive fields. Although there have been extensive works on bias assessment in English, such efforts are rare and scarce for a major language like Bangla. In this work, we examine two types of social biases in LLM generated outputs for Bangla language. Our main contributions in this work are: (1) bias studies on two different social biases for Bangla, (2) a curated dataset for bias measurement benchmarking and (3) testing two different probing techniques for bias detection in the context of Bangla. This is the first work of such kind involving bias assessment of LLMs for Bangla to the best of our knowledge. All our code and resources are publicly available for the progress of bias related research in Bangla NLP.