Social Bias in Large Language Models For B angla: An Empirical Study on Gender and Religious Bias (2025)

Abstract: 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.

## introduction the rapid advancement of large language models (llms) has significantly impacted various domains, particularly in social influence and the technology industry @xcite . given their growing influence, it is crucial to ensure llms are free from harmful biases to avoid legal and ethical issues @xcite . in the context of computing/socio-technical systems, bias refers to the unfair and systematic favoritism shown towards certain individuals or social groups, often at the expense of others, resulting in discriminatory outcomes @xcite . hence, analyzing bias and stereotypical behavior in llms is vital for identifying and mitigating existing biases. bangla, the sixth most spoken language globally with over 230 million native speakers constituting 3% of the world's population foot_1 , has remained underrepresented in nlp literature due to a lack of quality datasets @xcite . this gap limits our understanding of bias characteristics in language models, including llms. historically, societal views in bangla-speaking regions have undervalued women, leading to employment and opportunity discrimination @xcite . additionally, the region's cultural and historical context between two major religions, hindu and muslim, makes bangla a valuable case study for examining religious biases as well. in this study, we pose the question, to what extent do multilingual llms exhibit gender and religious bias in bangla context?. to address this, we present: (1) a curated dataset specifically designed to detect gender and religious biases in bangla, (2) detailed bias probing analysis on both popular and state-of-the-art closed and open-source llms, and (3) an empirical study on bias through llmgenerated responses. our findings reveal significant biases in llms for the bangla language and highlight shortcomings in their generative power and understanding of the language, underscoring the need for future debiasing efforts and better bangla specific finetuning of llms. ## related work existence of gender bias has been exposed in tasks like natural language understanding @xcite @xcite and natural language generation @xcite @xcite . benchmarks such as winobias @xcite and winogender @xcite have been used to measure gender biases in original corpus final corpus question creation [pii removal from sentence and create question] refined corpus reviewer agreement selection procedure gpt-3.5-turbo lms. preliminary studies on religious and ethnic biases are done in some works @xcite @xcite . works like @xcite provide frameworks and datasets for different types of biases including gender and religion. indibias @xcite , a benchmark in indian context, has been introduced to measure socio-cultural biases in llms. recent studies have conducted experiments on determining gender stereotypes in llms @xcite @xcite and debiasing techniques @xcite , but most of them are on english. there are a few works on multilingual settings @xcite , but such efforts are not common for bangla. the most preliminary work on bangla bias detection is found in the works of @xcite , that includes static and contextual embeddings. effectiveness of varied probing techniques for extracting cultural variations in pretrained lms has been discussed in @xcite . ## linguistic characteristics of bangla pronouns unlike english and similar languages, bangla lacks gender-specific pronouns (e.g., he, she). instead, bangla employs common pronouns that are used interchangeably for both male and female genders in both singular and plural forms. moreover, the structure of bangla sentences does not change in terms of verbs or other grammatical elements to indicate the gender of the subject, as is the case in languages like hindi or spanish. as a result, sentences in bangla that do not include gender-specific nouns or proper names are inherently gender-neutral. ## data we use two strategies for llm probing: template based and naturally sourced. the template-based approach uses curated templates for gendered persona or religious group predictions for bias evaluation. naturally sourced sentences, on the other hand, are used to make explicit predictions about groups or genders, helping to understand the llm's ability to interpret natural scenarios. we explain the two techniques as follows: template based: we create semantically bleached templates with placeholders for specific traits, filled with adjective words from categories like personality, outlook, communal, and occupation (see figures 6 and 9 in appendix). the adjective categories and words were validated by native bangla-speaking authors. to explore the effect of occupation on role prediction, we intermix professions with traits in the templates. examples in the placeholder column of figure 9 illustrate the process. care was taken to avoid stereotypes, ensuring all adjectives and occupations were equally probable for any gender or religious community. for gender detection, the templates employed gender-neutral pronouns of bangla, along with simple and context-independent sentences to obscure any clues about the gender of the person being referred to. similarly, for detecting bias related to religious communities, the templates used common, non-specific pronouns (e.g., they/them) and avoided any contextual or identifying details that could hint at the religious affiliation of the individual mentioned in the prompt. in total, we have 2772 template sentences by combining both the categories (see appendix 4 for detailed statistics). naturally sourced: the workflow of preparing the corpus for naturally sourced sentences is illustrated in figure 1 . we use the bibed dataset @xcite , specifically the explicit bias evaluation (ebe) data for naturally occurring scenarios. the sentences are structured in pairs, each containing one identifying subject from a group of either male-female words (for gender) or hindu-muslim words (for religion). figure 7 (in the appendix) illustrates how sentences are grouped into 'gender' prompt chatgpt-3.5-turbo root-pair sentence from ebe dataset no yes is the pair semantically consistent? select the root sentence reject the root sentence remove the subject of the sentence and convert to qa format prepare prompt for llms and 'religion' biases. it provides original (root) sentences, paired sentences with altered gender or religion entities, and the modifications necessary to transform them into data points. an important limitation of the bibed dataset is that many sentences are not equally probable for both contrasting identities due to issues such as contradictory historical facts, entity-specific information not applicable to the other, incorrect identification of gender or religion entity in the root sentences, or lack of moderation. examples of these non-applicable scenarios are shown in figure 8 (in appendix). to address this, we manually curated sentences to ensure equal applicability to both identities (see appendix c for details). each selected root sentence was transformed into a data point by removing the main identifying subject (malefemale for gender or hindu-muslim for religion) and converting it into a bias detection prompt. examples of the final prompt format are provided in the modification column of figure 7 . the prompt creation workflow is illustrated in figure 2 . after curation, 2416 pairs were retained for gender and 1535 for religion. ## meta-llama/meta-llama-3-8b-instruct 4 gpt-3-5-turbo 5 gpt-4o 6 anthropic/claude-3.5-sonnet to reduce randomness, we set the temperature very low (@xmath0.1) and restrict the maximum response length to 128. since bangla is a low resource language, not many models could generate the expected response we required. some of the open source models that we used but failed to get presentable results are mentioned in the limitations section. ## conclusion to summarize, our study investigates gender and religious bias in multilingual llms within the context of bangla, utilizing two distinct probing techniques and datasets. the results reveal varying degrees of bias across models and underscore the need for effective debiasing techniques to ensure the ethical use of llms in sensitive banglalanguage applications. additionally, the findings highlight the importance of developing linguistically and culturally aware frameworks for bias measurement. future research could focus on expanding the dataset to include non-binary genders, additional religious groups, and nuanced sociocultural contexts to better capture the diversity of banglaspeaking regions.