An Empirical Study of Gendered Stereotypes in Emotional Attributes for B angla in Multilingual Large Language Models (2024)

Abstract: The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there’s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.

## introduction humans display a wide range of emotions in their daily lives, which are integral to human intelligence and closely linked to personality and character. given the diversity of these emotional expressions, it is important to explore whether emotional patterns adhere to gender stereotypes. we define gendered emotional stereotypes as the generalization of expected emotional responses based on a person's gender in specific situations. emotions significantly impact how individuals conceptualize themselves and respond to stimuli @xcite , making bias in this context particularly harmful. historically, societal views toward women in bangla-speaking regions have often been regressive and undervaluing @xcite . evidence of discrimination in employment and opportunities @xcite underscores prevalent harmful stereotypes. these stereotypes depict women as inherently vulnerable, overly emotional, and more suited to roles requiring empathy and care @xcite . conversely, men are perceived as aggressive, resilient, and less capable of handling tasks that necessitate emotional sensitivity and compassion. such deeply ingrained stereotypes risk being perpetuated by large language models (llms). therefore, it is essential to examine these effects given the growing use of llms. recent works have shown that persona-based prompting can be utilized to reveal stereotypes in llms @xcite . we utilize the persona presuming capabilities of llms to attribute emotions to gendered personas in a specific scenario in order to evaluate the presence of gender stereotypes. to be specific, the model would be assigned a persona and given a scenario to reply with an emotion attribute. in a bias free setup, we would expect the emotions to be uniformly distributed irrespective of gender. our contributions in this paper include, (1) the first study that examines gender bias and stereotypes in emotion attribution in state-of-the-art llms for bangla language, (2) a quantitative analysis of around 73k llm generated responses for over 6k online comments collection for bangla that covers both male and female personas, and (3) a qualitative analysis of the generated responses and resulting nuances due to instruction variability. our study suggests the presence of gender stereo-types in model responses that could cause harm to a certain demographic group in emotion related nlp tasks. ## bias statement various definitions of bias exist across research, as detailed in @xcite . in this work, we focus on stereotypical associations between masculine and feminine gender and emotional attributes in llm responses. if a system consistently associates specific emotions with particular genders, it perpetuates harmful stereotypes, such as women being perceived as experiencing more guilt, shame, or fear, or men as experiencing more anger or pride. this representation poses risk of discrimination on the basis of gender and put obstruction on the natural expression of emotions. our study aims to illuminate gender-emotion correlations in llm responses for bangla language. ## related work since historical times, the relationship between gender and emotions has endured across linguistic and geographic barriers, deeply ingrained in society perceptions. numerous academic studies have investigated the historical foundations of gendered emotional stereotypes, demonstrating their persistent existence across diverse historical periods and cultural contexts (e.g., @xcite ; fischer and manstead ( 2000) ). gender bias in language models has been extensively explored, initially focusing on static embeddings (e.g. @xcite , @xcite ) before shifting to contextual word embeddings (e.g. @xcite ; @xcite ) with the rise of transformer-based language models. these works provide the baseline results and introduce popular bias measuring techniques. the work of @xcite stands out as one of the first to consider model response analysis for bias measurement. efforts to measure gender stereotypes in natural language generation tasks yield notable results as well @xcite @xcite . benchmarks such as winobias @xcite and winogender @xcite have been used to measure gender biases in lms. studies on gender bias and stereotypes in llms were studied in detail in @xcite ; dong et al. (2024); zhao et al. (2024). the techniques used here mainly comprise of template-based prob-ing and token prediction based analysis. similar efforts along with de-biasing techniques were discussed in ranaldi et al. (2023); gallegos et al. (2024). notably, del arco et al. (2024) provides compelling evidence supporting the presence of gendered emotions in llms. the research in bangla nlp had gained pace over the past years. works of bhattacharjee et al. (2022); hasan et al. (2021, 2020); akil et al. (2022) has contributed to the development of bangla in the domains of natural language understanding and dataset enrichment. the first notable work on gender stereotypes in bangla is done by sadhu et al. (2024), where they provide baselines for different bias measurement techniques. early research on emotional attributes in bangla primarily involved creating emotion datasets and multi-label classification tasks. examples of such works are irtiza tripto and eunus ali (2018); das et al. (2021); islam et al. (2022). we provide the first study to evaluate gender bias regarding emotional attributes in multilingual llms for bangla. ## data we use the annotated dataset from @xcite . it is a public dataset containing public comments from social media sites covering 12 different domains such as personal, politics and health, labeled for 6 fine-grained emotion categories of the junto emotion wheel (love, fear, anger, sadness, surprise, joy) (see appendix a). we refine the data for our use such that we extract examples that have the two following properties: 1. expresses an event or statement or description 2. does not include any statements or examples explicitly mentioning any emotions for the first point, we eliminated the comments which are very short and have no semantic values (like "ok", "fine" etc.). for the second case, we ## experimental setup our experiment focuses on exploring the capacities of large language models (llms) in emotion attribution tasks. in this task, the objective is to identify the primary emotion of a given comment in relation to a specified persona. we adopt a zero-shot learning (zsl) approach for our model setup, meaning no training examples are provided beforehand. this decision aims to prevent any pre-existing bias from influencing the model's judgments. through zsl, we investigate whether llms demonstrate gendered emotional stereotypes. ## conclusion in this study, we examined gender stereotypes in emotion attributes across three state-of-the-art multilingual llms (both open and closed source), which is the first study of this kind for the bangla language. our analysis was conducted on a dataset of over 6,000 online comments, generating completions for male and female personas without losing generality of the research topic. our quantitative analysis reveals that the models consistently exhibit gendered emotion attributions. a subsequent qualitative analysis suggests these variations are influenced by prevalent gender stereotypes, aligning with findings from psychology and gender studies on gender-based emotional stereotypes. these findings raise concerns about the direct application of llms in emotion-related nlp tasks, especially considering their potential to reinforce harmful stereotypes. additionally, it is important to note that the models used in this study were not fine-tuned for bangla-specific tasks, particularly the open-source model. therefore, it is crucial to implement de-biasing measures during the finetuning process for bangla language tasks. we advocate for further research in this area, specifically focused on the bangla language, and the development of frameworks for bias benchmarking to ensure more equitable and accurate nlp applications.