Language Models Predict Empathy Gaps Between Social In-groups and Out-groups (2025)

Abstract: Studies of human psychology have demonstrated that people are more motivated to extend empathy to in-group members than out-group members (Cikara et al., 2011). In this study, we investigate how this aspect of intergroup relations in humans is replicated by LLMs in an emotion intensity prediction task. In this task, the LLM is given a short description of an experience a person had that caused them to feel a particular emotion; the LLM is then prompted to predict the intensity of the emotion the person experienced on a numerical scale. By manipulating the group identities assigned to the LLM’s persona (the “perceiver”) and the person in the narrative (the “experiencer”), we measure how predicted emotion intensities differ between in-group and out-group settings. We observe that LLMs assign higher emotion intensity scores to in-group members than out-group members. This pattern holds across all three types of social groupings we tested: race/ethnicity, nationality, and religion. We perform an in-depth analysis on Llama-3.1-8B, the model which exhibited strongest intergroup bias among those tested.

## introduction figure 1 : task setup with in-group and out-group examples. we introduce perceiver and experiencer roles to define the intergroup relationship, where it is in-group when they are from the same social group. the perceiver is modeled by the llm persona and the experiencer is specified in the task context. each role falls into one of the race or ethnicity, nationality, and religion categories. the social group is specified with identity names under the category. we replace the identities of perceiver and experiencer to study intergroup bias. in psychology, the intergroup process-how people perceive and interact with others who are members of the same group (in-group) or members of a different group (out-group)-has been widely studied. research shows that people view social in-group and out-group members with different empathic feelings and emotional intensities @xcite @xcite @xcite , and this behavior further shapes the intergroup relations @xcite . for example, a person might feel more warm and act more friendly toward another person from their home country, but act indifferently-or similarly with less intensity-toward a person from another nation. appropriately addressing empathic failures helps reduce conflicts between groups and reduce out-group discrimination @xcite . in this paper, we study intergroup bias in large language models (llms) by asking: do llms reflect human-like empathy gaps between social in-groups and out-groups? to test the question, we formulate an emotion intensity prediction task, 2 as shown in figure 1 . in this task, we simulate a scenario in which the llm's assigned persona ("the perceiver") reads a short narrative of an experience that a person ("the experiencer") had which caused them to feel a particular emotion; the perceiver (llm) is then prompted to predict the intensity of the emotion felt by the experiencer on a numerical scale. to compare in-group and out-group empathy, we manipulate the llm inputs to assign the perceiver and experiencer a social group identity based on either race/ethnicity, nationality, or religion. we compare the predicted intensities when the perceiver and experiencer belong to the same social group (in-group) or different social groups (out-group), finding higher average intensities in the former. to illustrate, consider the scenario in figure 1 : i felt sad when i received job rejections, where "i" refers to the experiencer. the llm's persona, a white perceiver, predicts a higher degree of sadness for a white experiencer than for a black experiencer in the identical scenario. while many papers have studied stereotypes and harms with language models, they typically consider the task from a single perspective of either how these models perceive other groups through their representations @xcite @xcite @xcite , or in downstream tasks how they are biased towards target groups @xcite @xcite @xcite , ignoring the intergroup cases when both the perceiver and target are present. our work builds on a few recent studies of intergroup perceptions in llms @xcite , which focus on relationships in politics or in sports. our primary contributions and findings are: (1) we study intergroup empathy bias with respect to group identities rooted in race/ethnicity, nationality, 2 empathy is complex and multidimensional, making it difficult to measure @xcite . however, in studying the intergroup empathy gap, intensity bias can serve as a lens, as suggested by @xcite . and religion. we study four broad race/ethnicity categories (with 18 corresponding group names), 21 nationalities, and five religions. (2) we show llms present in-group and out-group emotion intensity differences, where llama-3.1-8b models show significantly higher intensities for in-group cases and overall lower intensities for minority groups. (3) we observe the intensities are affected by the cultural and historical factors which might further enlarge the tension between groups. similarly, people share and understand other's emotions with empathy, but treat others differently based on identities. @xcite defines intergroup empathy bias as: "the tendency not only to empathize less with outgroup relative to in-group members, but also feel pleasure in response to their pain (and pain in response to their pleasure)" empathy failures might introduce intergroup conflicts and discrimination @xcite @xcite . research on interpersonal relationships @xcite @xcite and neurocognitive understanding @xcite support the importance of studying this concept in group contexts @xcite . in our work, we use perceived emotion intensities as a measure of empathy to compare relative levels of in-group versus out-group empathy. social identity and persona. social identities have been studied when users interact with chat-bots @xcite . people react differently due to the target identities with hate speech @xcite . llms might thus learn in-group favoritism representations when prompted with "we are" @xcite . while there are approaches discussing the bias mitigation @xcite , new challenges are introduced with llms @xcite . personas, or fictional identities that llms have been instructed to adopt, have been used to study a variety of social phenomena in llms. it can be a way to understand the truthfulness of llms @xcite , but possibly lead to in-group bias under a multilingual setting @xcite . in this work, we focus specifically on intergroup empathy bias as a form of intergroup prejudice rooted in social identities that may be studied in llms with the use of such personas. ## general methods we construct an emotion intensity prediction task to measure the impact of in-groupness and outgroupness on model outputs. our specific task has the following components: the emotion, the emotional situation, the social group of the experiencer (who is experiencing the emotion), and the social group of the perceiver (who observes the experiencer). we instruct models to predict the intensity of a specific emotion. for example, in figure 1 , the model needs to predict the intensity of sadness in a job rejection scenario given variable experiencer and perceiver social identities. ## results on in-group and out-group emotion intensity gap with the perceiver in-group identity or experiencer in-group identity). we discuss both in detail below. race or ethnicity, nationality and religion groups all show higher predicted intensities for in-group pairs. from the summarized δ in the majority of exceptions to this are with the larger llama-70b, where, especially for religion, we sometimes see a negative gap (though often small in magnitude). the average empathy gap ranges from 0.13 (llama-70b) to 2.11 (llama-8b), with mistral (0.77) and qwen (1.20) in the middle. for race or ethnicity groups, where we test identity name variations for the same social group, in llama-8b models, we consistently observe a clear and distinct block-diagonal pattern (figure 3 and the first row of figure 2 ), where a lower gap is seen for in-group comparisons than for out-group comparisons. we also see that when the perceiver is white, the out-group gap is generally lower; this is likely due to a defaulting effect where unspecified perceiver is "assumed to be" white @xcite . for other models, foot_6 while the deviation is small, masked cells are mostly in diagonal blocks, showing out-group predictions might follow different distributions from in-group pairs. ## analysis on different perceptions of social groups we conduct a in-depth analysis with llama-3.1-8b as it shows the strongest gaps between groups, aiming to understand how groups and intergroup relationships are learned differently. ## discussion and conclusions our paper focuses on uncovering social biases along two-axes rather than the more standard single-axis "disaggregated evaluation" paradigm that has gained significant traction in evaluating model fairness. we introduce the intergroup framework to study the intergroup empathy gap predicted by language models. our results show llms tend to predict higher emotion intensities for in-group cases regardless the group categories in race or ethnicity, nationality, or religion. by taking a deeper look on llama-3.1-8b results, we observe models represent social groups differently with possible historical factors and cultural effects. with the complex intergroup perceptions in human and further learned by language models, it is important to think a step further on the potential harms. considering people are relying more on llm-mediated communication, the intergroup prejudice could negatively impact how people interact with each other unconsciously. though psychologists propose putting ourselves in other people's shoes can reduce the bias in interpersonal communication @xcite , it is not clear about the meaning of "perspective-taking" when it comes to language models. we need to study where they learn the intergroup bias so we can intervene the downstream decision-making tasks such as hiring @xcite . however, we don't mean the intergroup empathy gap always brings harms. people treat others differently based on the social group memberships with meanings. it can help in-group cohesion and live a fulfilling life with enough resources and physiological support. moreover, individuals from underrepresented groups may already face discrimination from dominant groups, and addressing the empathy gap in communication without care could potentially exacerbate existing power imbalances. we hope our community can be more aware of intergroup bias while pursing more intelligent general ai systems.