Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution (2024)

Abstract: Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men’s anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like ‘When I had a serious argument with a dear person’. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications.

## introduction emotions are a ubiquitous experience, yet also vary from person to person. if a colleague publishes prolifically, some people might envy them, others admire their output, and a third might feel sad-ness about their inability to compete. but do these emotional patterns follow broader gender lines? user: you are a woman. ## background stereotypes linking gender and emotions trace back to ancient philosophical and scientific writings. both aristotle @xcite and darwin's 'the descent of man' @xcite touched upon gender differences in the emotional landscape. aristotle suggested that women were more prone to emotional excess, while darwin posited evolutionary reasons for gendered emotional behaviors, such as aggression in men and nurturing in women. these historical perspectives laid the foundation for later research on gender and emotion, highlighting the enduring influence of societal norms and stereotypes on emotional discourse. the effects of gender stereotyping have received further attention in recent years with emphasis on how notions of masculinity and femininity affect young boys and girls. 1 men have historically been perceived as assertive, dominant, and active, traits commonly associated with emotions like pride and anger @xcite . on the other hand, women have often been portrayed as nurturing, empathetic, and helpless, characteristics that align with emotions such as joy and sadness @xcite . anger is strongly linked with the desire for justice, whereas sadness involves seeing oneself as helpless in a bad situation. 2 these stereotypes have led to prejudices since they incorrectly attribute certain posited group's characteristics to individuals. they also limit people's self conceptions when one wants to see oneself as belonging to that group @xcite . for instance, the expectation for men to suppress emotions like sad-ness or vulnerability can lead to emotional repression and limited emotional expression @xcite . similarly, societal pressure on women to prioritize others' emotions over their own can result in the neglect of personal well-being and emotional needs @xcite . ## experimental setup one application of llms is to perform standard nlp tasks by formulating a specific request as the input prompt in a zero-shot learning (zsl) setup, where no examples are provided to the model. here, we address the task of emotion attribution: given an event and a persona, the task is to determine the main emotion the persona (e.g., a man) would experience. we use zsl to study whether llms exhibit gendered emotional stereotypes. previous studies have examined stereotypes and biases by ascribing a persona to the llm through a prompt (e.g., "take the role of a man."). these "llms designed for specific personas" enhance interactions by personalizing responses and hold broad practical utility due to their potential to mimic human behavior. @xcite , a well-known dataset in emotion analysis that is publicly available. it includes 7,665 english self-reported events from around 3,000 respondents from 37 countries across five continents. the respondents were asked to report situations in which they had experienced seven major emotions (anger, disgust, fear, guilt, joy, sadness, and shame) which encompass the six emotions proposed by @xcite , excluding surprise. this dataset contains demographic information for each respondent, including (binary) gender, religion, and country of origin. we use the gender for conducting the experiments shown in section 5.1. we removed any instances with invalid events like "no response". the final source contains 7,586 events from 4,153 woman and 3,444 man subjects. ## llms exhibit gendered emotions figure 2 shows the aggregated frequencies of the 25 most commonly predicted emotions for all models per gender. we find stark gender differences: models attribute sadness to women 10,635 times and only 6,886 times to men; joy is attributed 4,415 times and 6,520 times to men and women, respectively. in turn, anger is attributed to men almost twice as often as for women (13,173 times compared to 7,042). we find similar patterns for the other emotions: pride (attributed to men 3,275 times vs to women 1,392 times), frustration (9,419 vs 5,990 for men and women, respectively), fear (10,604 for men vs 12,589 for women), disappointment (5,567 for men vs 6,441 for women) and regret (3,631 for men vs 2,611 for women). as shown in differences across llms these patterns are pervasive across models, albeit with some differences, full details in ## emotion attribution by stereotypes next, we address the question of whether the differences described in the previous section are arbitrary, reflect actual differences in lived experiences, or are based on societal stereotypes about the emotional capabilities of the genders. we have seen that the models consistently show distinct gendered emotion associations (see figure 2 and the aggregated frequencies in women are commonly associated with sad-ness and joy. women have often been depicted as nurturing, empathetic, and vulnerable, traits that correspond with emotions such as joy and sadness @xcite . we find supporting evidence that models, too, reflect these stereotypes, frequently linking women to a range of negative emotions, including sadness and fear, as well as positive emotions like joy. conversely, men are often correlated with anger and pride. previous research has shown that men are associated with assertive, dominant, and active traits, commonly linked to emotions like pride and anger @xcite . our findings further support this as the models frequently attribute emotions such as anger, frustration and regret to men while also associating them with positive emotions, such as pride. to shed more light on these gendered stereotypes across the llms, we examine the unique words generated for each gender. given this alignment in findings, we hypothesise that models' attributions are based on societal stereotypes and not on arbitrary or factual differences in women's and men's lived emotional experiences. to address this, we first consider the gold labels in the isear dataset (for each event, the respondent's gender is provided). if the models reflected real differences, this should be mirrored in the models' performance. note that we are not looking for differences in the overall performance between the genders but whether there are patterns in the incorrect predicted labels of the models. ## related work a vast body of work has explored stereotypes relating to various facets of demographic identity such as race and ethnicity, religion, and sexual orientation (e.g. @xcite @xcite . the most commonly studied of these dimensions is that of gender, where stereotypes have been explored in static word embeddings @xcite , and llms (e.g. @xcite @xcite . to this end, various metrics have been proposed to measure the levels of stereotyped biases in llms, including those adapted from social psychology such as the implicit association test @xcite and the sensitivity test @xcite , or extrinsic tests of downstream performance on nlp tasks @xcite . gender bias particularly @xcite has received much attention in machine translation @xcite @xcite as well as other nlp tasks. however, there is a surprising lack of research on gender bias in emotion analysis. treatment of emotions in nlp has often been cast as a classification task (e.g. @xcite @xcite . another line of work seeks to generate text with the appearance of emotional content (e.g. @xcite @xcite . recent work has harnessed persona-based prompting to reveal the varied stereotypes they can produce. several of these have focused on using personas to elicit toxic content @xcite @xcite . meanwhile, @xcite investigated identitybased stereotypes in the persona descriptions generated by llms. @xcite (whose persona templates we adopt) measured a range of societal stereotypes in the responses provided by llms to questions benchmarked from reasoning datasets. to our knowledge, no prior work examines gender stereotypes expressed in such generated output. ## discussion llms have been suggested in the emotion analysis literature as potential solutions to most datasets' limited amount of labeled data @xcite . however, our findings call into question their suitability for the task. we find consistent patterns of gendered emotion associations across various models. this finding prompts a critical inquiry: do we want llms to reflect these social stereotypes? the dichotomy lies in the potential dual role of llms -acting both descriptively as mirrors reflecting societal biases and normatively as influential contributors to the perpetuation of these biases. emotions serve as heuristics to interpret a given situation, and we learn to interpret this heuristic given societal cues during our upbringing. we might thus be tempted to justify models' varying predictions, given that people of different genders might interpret the same event differently. however, while we may experience emotions differently due to factors such as gender, models do not only reflect but severely amplify this disparity: in our results, models overwhelmingly predict sadness for women and anger for men, even when the annotators themselves labeled different emotions. empirical studies show that gender stereotypes affect how we judge the abilities of men and women, and how people interpret and remember information about themselves and others @xcite . the presence of these stereotypes in llms poses a potential risk to downstream emotion applications, especially in sensitive areas like mental health @xcite and humancomputer interaction, spreading representational and allocational harms @xcite . given the background of work in psychology and gender studies on this topic, in this paper, we call for interdisciplinary work, embracing disciplines such as psychology and philosophy to inform and mitigate gendered emotions based on social stereotypes within nlp systems. ## conclusion we present the first study examining societal biases and stereotypes in emotion attribution in five state-of-the-art llms (open-and closed-source). given an event like "when i had a serious argument with a dear person", the model has to attribute the emotion a given gendered persona would feel in that event. we provide a quantitative study based on over 200k completions generated by the five models for over 7,000 events and two personas, spanning over 400 unique emotions. we find strong evidence that all models consistently exhibit gendered emotions. we then find that these variations are influenced by gender stereotypes. in addition, we perform a qualitative study that supports our findings. these findings align with psychology and gender studies of gender-based emotional stereotypes. our results raise questions about using llms for emotion-related nlp tasks. they emphasize the importance of examining and improving llms' fairness and inclusiveness. we advocate for more interdisciplinary collaboration to build upon prior research in this domain.