Are LLM s Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model’s Empathy (2025)

Abstract: Large Language Models’ (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs’ cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model’s empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.

## introduction large language models have become prevalent in human-facing applications, especially those involving healthcare and mental health @xcite ). an llm's ability to conduct naturalistic conversation is rooted in its understanding of a user's situational, contextual and emotional expression @xcite . this understanding helps build trust with the user who, in turn prefers models that demonstrate empathy in conversations @xcite . empathy is defined as the act of perceiving, understanding, experiencing, and responding to as seen above, responses to a female from a confucian culture are more culturally grounded, while those to a male from an english-speaking culture focus on problem-solving, highlighting variation in cognitive empathy as well as affective empathy. the emotional state and ideas of another person @xcite . emotional experiences are deeply personal and shaped by an individual's background and lived experiences @xcite . an able and democratic ai must understand and respond to a person empathetically @xcite , while being equitable and relative to the person's background and identity @xcite . recent studies have shown that llms achieve higher-than-human emotional quotient scores @xcite . though they don't possess an internal state to experience this emotion @xcite , llms can respond to users appropriately @xcite . llms have been tested on their capability on fine-grained tasks, such as the ability to correlate events and emotions @xcite or the ability to showcase emotional understanding and its application in multi-lingual contexts @xcite . however, when centring the user's expressed emotional experience, which often comprises one's cultural, age, and gender experiences @xcite , research has shown that the model's empathy is either extremely generic toward certain groups @xcite or the models exhibit strong societal stereotypes @xcite toward others. often these stereotypes are likely to highlight more positive signals than negative @xcite . when llms show deviating behavior, we wonder, what group or attribute are models already aligned with? additionally, in a real-world interaction, a user's persona is a composition of multiple attributes. sometimes these attributes can be contrasting, for example, the persona of an old man. men are stereotyped to express a higher anger intensity @xcite , while older adults express less anger @xcite . in this case, does this composition of multiple attributes deter or enhance an llm's empathetic ability? guided by these questions, we aim to understand how an llm positions itself while understanding and responding to emotions, and whether this positioning is comparable to real-world interactions. in this study, we thus ask the following questions: rq1: is the llm's capability to show empathy relative across various user personas? to what extent is this variation affected by the intersectionality of cooccurring attributes? rq2: does an llm's variance in empathy capabilities align with real-world emotional experiences? rq3: which attribute or composition of attributes is reflective of the llm's neutral state? to answer these questions, we conduct a multidimensional analysis across 3 demographic attributes of culture, age, and gender, using 4 llm families on the isear dataset @xcite . as illustrated in figure 1 , the isear dataset comprises personal reports of emotional experiences from users with diverse personas. in our setup, the model engages in a simulated conversation, where it receives the user's persona and emotional experience as input. it is then tasked with both predicting the expressed emotion and generating a response tailored to that specific persona. our findings reveal that llms exhibit substantial variation across these demographic dimensions. this variation often reflects stereotypes documented in the literature, and is further influenced by the type of attribute as well as the presence or absence of additional contextual personas. ## related work llms are being extensively evaluated in all aspects of healthcare, specifically mental health @xcite , such as detecting disorders related to mental health @xcite , providing support @xcite @xcite , and helping de-stigmatise mental health @xcite . however, this use has been questioned as they show poor emotional misalignment with humans @xcite . ## experiments to evaluate how well various llms demonstrate both affective and cognitive abilities across diverse user backgrounds, we simulate real-world ## results in the previous section, we were able to understand that adding attributes can hinder the ability of llms to empathize with the user, especially when added as compositional personas. in this section, we investigate whether these variations are expected and how they position themselves with respect to the real world and the model itself. ## discussion the increasing use of large language models (llms) across diverse domains and user groups @xcite necessitates a critical evaluation of their equitable and empathetic performance across personas. empathy in recognizing emotions (affective empathy) and responding appropriately (cognitive empathy) is central to human-ai interaction @xcite . however, our analysis suggests that current llms do not exhibit uniform empathetic behavior across demographic attributes, challenging assumptions about their fairness and inclusivity @xcite . our study, spanning 4 llms and 315 personas from combinations of age, gender, and culture, reveals notable disparities. personas representing confucian cultures, younger users (0-17), and gender-queer identities often receive responses that diverge from those directed at more dominant groups like english speaking, male. while they do loosely mirror real-world patterns, this is not always beneficial, as we see in cultures like confucian, african-islamic and latin american overemphasizing cultural contexts at the expense of emotion depth, showcasing stereotypical understanding. our findings present few important questions about empathetic alignment for diverse personas. what does equitable empathy entail for all groups irrespective of their dominance in the model's internal state? should llms provide the same empathetic response to all users, or is personalisation of these responses a valid goal? finally, how do we ensure that these personalised responses do not reinforce harmful stereotypes? our work underscores the need for inclusive and culturally aware evaluations of llms. we advocate for an alignment framework that can quantify and ensure that the model is empathetic while being emotionally intelligent and fair. ## conclusion we present a comprehensive and novel study evaluating whether large language models (llms) exhibit equitable empathy across diverse user personas. our analysis spans four llms and 315 unique persona combinations formed from age, culture, and gender attributes. our findings reveal that llms' empathetic responses are often shaped by contextual attributes and are influenced by societal stereotypes. certain demographics receive more consistent or favorable empathy, while others, particularly underrepresented groups, experience notable misalignment. these disparities are driven by both the models' internal representations and broader cultural biases embedded in it's learned parameters. through a multi-dimensional quantitative and qualitative analysis, we uncover key patterns underlying these variations. our work highlights the critical need for more responsible, context-aware deployment of llms in user-facing applications. we advocate for future efforts to develop empathetic alignment frameworks that ensure fairness and inclusivity in ai behavior.