Divine LL a MA s: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models (2024)

Abstract: Emotions play important epistemological and cognitive roles in our lives, revealing our values and guiding our actions. Previous work has shown that LLMs display biases in emotion attribution along gender lines. However, unlike gender, which says little about our values, religion, as a socio-cultural system, prescribes a set of beliefs and values for its followers. Religions, therefore, cultivate certain emotions. Moreover, these rules are explicitly laid out and interpreted by religious leaders. Using emotion attribution, we explore how different religions are represented in LLMs. We find that:Major religions in the US and European countries are represented with more nuance, displaying a more shaded model of their beliefs.Eastern religions like Hinduism and Buddhism are strongly stereotyped.Judaism and Islam are stigmatized – the models’ refusal skyrocket. We ascribe these to cultural bias in LLMs and the scarcity of NLP literature on religion. In the rare instances where religion is discussed, it is often in the context of toxic language, perpetuating the perception of these religions as inherently toxic. This finding underscores the urgent need to address and rectify these biases. Our research emphasizes the crucial role emotions play in shaping our lives and how our values influence them.

## introduction the people of toraja in southern indonesia are known for their elaborate funeral rites, keeping embalmed bodies of deceased family members at home for months or years before burial, and periodically exhuming them for family celebrations @xcite . death and loss are unifying ex- ## background emotions can be broadly categorized into affect program theories and propositional attitude theories @xcite . affect programs relate to fundamental, universally recognized emotions like anger, disgust, joy, sadness, and fear. in contrast, propositional attitude theories encompass a broader spectrum of more cognitively intricate emotions, such as guilt, shame, pride, and gratitude. cognitive evaluations heavily influence these complex emotions and are believed to vary significantly across cultures. religion and spirituality play a significant role in cultivating and expressing these complex emotions, particularly within the framework of propositional attitude theories @xcite . religious traditions provide contexts and practices that nurture sacred or spiritually significant emotions. 2 sacred emotions sacred emotions are more prevalent in religious settings like churches, synagogues, and mosques than non-religious ones. they are also more likely to arise from spiritual or religious activities such as worship, prayer, and meditation rather than from non-religious pursuits. people who identify as religious or spiritual are more prone to experiencing these emotions than those who do not @xcite . these emotions, including gratitude, awe, reverence, love, and hope, are traditionally fostered by religious and spiritual traditions worldwide @xcite . gratitude, for instance, is described as "the willingness to recognize the unearned increments of value in one's experience" @xcite , fundamentally seen as an emotional response to receiving a gift. awe is characterized by sensitivity to greatness, often accompanied by feeling overwhelmed by the object of greatness. reverence, on the other hand, is defined as "an acknowledging subjective response to something excellent in a personal (moral or spiritual) way, but qualitatively above oneself" @xcite . wonder arises from encountering something novel and unexpected, perceived as intensely powerful, real, true, or beautiful @xcite . lastly, hope, considered a theological virtue alongside faith and charity, holds significance in christian theology as anticipation of the future kingdom of god @xcite . ## experimental setup data we use the self-reported events collected from the international survey on emotion antecedents and reactions (isear) @xcite , gathered from a diverse group of english-speaking respondents. participants were asked to recount situations that elicited seven primary emotions: (anger, disgust, fear, guilt, joy, sadness, and shame) which encompass the six emotions proposed by @xcite , excluding surprise. each self-report provides a detailed account of how the situation was appraised and the subsequent reaction. the resulting dataset consists of 7,586 events. ## results we analyze the results from two primary lenses: the refusal rates exhibited by the llms across different religions (section 4.1) and the emotional attributions made by the models towards various religions and levels of practice (section 4.2). notably, we find significant differences in the proportion of refusals by the models across religions, with distinct patterns emerging. in addition, we observe substantial differences between models, religions, and religious observance in terms of the emotional attributions made, with varying distributions of emotions attributed to each. we delve into each of these findings in greater detail: ## related work religious texts like the bible have been broadly used in nlp not as a resource to study and model religion itself but as convenient sources of translation and structured texts @xcite . instead, religion as an attribute has received relatively little attention in nlp. though some work has studied the particularities of religious language (e.g. @xcite @xcite ) and language about religion @xcite , most work in nlp surrounding religion has focused on the study of hate speech (e.g. @xcite and stereotypes in language models (e.g. @xcite @xcite . @xcite study religious bias in llms through prompt completion, analogical reasoning, and story generation, particularly surrounding muslim and jewish people, and find that models like gpt-3 hold negative stereotypes about these groups. most of this work focuses on a small subset of religions, particularly islam and christianity, with some work also considering atheism, bud-dhism, and hinduism. cultural bias in llms has received increased attention recently as it affects many nlp tasks. for example, palta and rudinger ( 2023 ## discussion in sum, we profile llms in terms of their representation of sacred emotions by leveraging personas. our work contributes to a small but growing body of literature on religion and nlp and to the larger area of bias and stereotypes in llms. on refusal rates: there is a general pattern where llama family models are better able to generate religious emotions but also exhibit higher refusal rates, particularly when it comes to muslims and jews. this suggests that the alignment process of these llms may unintentionally cause either biases or a refusal to discuss certain religions. we also ascribe this to the existing literature on religion focusing on islamophobia and anti-semitism, as well as current world issues surrounding conflicts and stereotyping. on sacred emotions: overall, we find that the models rarely generate sacred emotions like awe and hope, with the exception of gratitude. for a snapshot of sacred emotions, see section 2. however, the models do attribute emotions based on religion and emotions that are tied to religious practices, like sorrow and remorse for catholics. notably, these emotions are not represented in emotion analysis datasets (plaza-del-arco et al., 2024), but models can still find relationships between emotions and religion. the models also ascribe these emotions more often to more devout adherents, further evidence of the tie. moreover, the models often cite each religion's scripture in their explanations, for example, citing the quran's teachings when explaining a muslim's attributed emotion in a given situation (for more examples, see stereotype or educated guess: although other demographic attributes may impact our emotional landscape, religion is very explicit about the type of emotions one should cultivate. in this sense, it is hard to tease apart stereotyping from religion's normative emotional guides. for example, compassion is key in buddhism, and one should always aspire to be more compassionate to achieve enlightenment. buddhism prescribes practices like loving-kindness meditation to develop more compassion. however, love, joy, and equanimity -the other three immeasurables -are equally important 4 but do not feature often in the models' attributions even when they would be more reasonable. in addition, in section 4.2, we showed that when it comes to hindus, the models switch from ahimsa to krodha (polar opposites). kroda is an emotion that is discouraged and should be treated according to hinduism. moreover, these models rarely generate like contentment or bliss, which are integral to hinduism @xcite . this shows a lack of nuanced modeling and points towards typecasting rather than an educated guess based on religious scripture. note that here we are considering the emotions religions encourage, not actual individuals' experiences. in general, though the models have captured some notions about the relationship between religion and emotions and the normative frameworks set by religion to guide our appraisals of events in our lives, they still leave room for improvement before they can be used for analysis or religious texts or other tasks. ## conclusion our study sheds light on the underexplored topic of religion in nlp and llms. we investigate how llms attribute emotions to various religious groups and uncover whether these attributions reveal discernible patterns rooted in biases and stereotypes. our results demonstrate that major religions prevalent in the us and european countries are portrayed with more nuance and depth, whereas 4 the four immesurables eastern religions like hinduism and buddhism are subject to stronger stereotypes. furthermore, judaism and islam are frequently stigmatized, with higher refusal rates in responses which portrays the two as inherently unsafe. this suggests a possible conflation of these religions with negative connotations in the training data. our findings emphasize the significance of exploring and addressing cultural biases in llms, particularly in the context of religion. finally, our research contributes to a deeper understanding of the intricate relationships between religion, culture, and emotions in llms, highlighting the need for more diverse and representative training data to ensure that llms can provide accurate and unbiased emotional attributions.