Presumed Cultural Identity: How Names Shape LLM Responses (2025)

Abstract: Names are deeply tied to human identity - they can serve as markers of individuality, cultural heritage, and personal history. When interacting with LLMs, user names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. In this work, we study name-based cultural bias by analyzing the adaptations that LLMs make when names are mentioned in the prompt. Our analyses demonstrate that LLMs exhibit significant cultural identity assumptions across multiple cultures based on users’ presumed backgrounds based on their names. We also show how using names as an indicator of identity can lead to misattribution and flattening of cultural identities. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.

## introduction names carry deep cultural and personal identity, playing a central role in human communication. sociological research indicates that names are imbued with culturally loaded meanings that can trigger stereotypes and discriminatory responsesevidence of which is seen in field experiments, where individuals with ethnically distinctive names receive fewer opportunities @xcite . however, names do not always equate to a singular cultural identity. people may have names that reflect heritage from one culture while having grown up in a completely different cultural context, such as in cases of immigration, diaspora communities, or multicultural families. in human interaction, there is usually a larger context or other cues that provide a signal to a speaker about the other person's identity. however, such cues may be missing when a user is interacting with an llm, making the limited information available in the prompts and stored in memory very important. large language models (llms) are increasingly being integrated into personalised applications like virtual assistants, where providing helpful suggestions requires tailoring responses to individual users. to build this understanding, models have to undergo a process of implicit personalisation, i.e., changing the answer based on implicit presumptions about the user @xcite . popular platforms offering virtual assistants also have features where they store 'memories' about the user (ope-nai, 2024b) or mimic the writing style @xcite to tailor the response to a specific user. when analyzing llm memory traces, openai (2024a) found that the most common single memory is: "user's name @xmath0, and that users often explicitly mention their own name in their interactions with models. therefore, names serve as a rich signal for personalisation to the models. however, erroneous presumptions about a name's associated identity can lead to biased or misleading personalisation, reinforcing stereotypes. llms are trained on vast and heterogeneous datasets -often comprising web-scraped text, literature, and digital communications -that inherently include personal information, linking names with various identifying attributes and identities @xcite . this linking leads to a name bias, which alters the output when a name is mentioned in the prompt @xcite . while prior work has examined gender and race presumptions based on names @xcite , there has been no work on investigating cultural presumptions in llms. examining name-based cultural presumptions reveals how models can perpetuate stereotypes, misrepresent cultural details, and flatten identities, yet this very examination also provides crucial insights for creating more equitable and culturally sensitive ai systems @xcite . in this paper, we study name bias with respect to cultural presumptions in llms with 900 names across 30 cultures and 4 llms and questions spanning multiple cultural facets including food, clothing, and rituals. we prompt llms with a name and different information-seeking questions, assessing if the response is personalised towards the culture the name is from by giving corresponding suggestions. our analysis shows strong evidence of cultural identity presumption and significant asymmetries in how llms associate names with cultural elements, with particularly strong biases for some cultures (e.g., east asian and russian names), while showing weaker associations for names from certain other cultures. finally, there is also substantial disparity between the names themselves, especially for names common across multiple cultures. names common across cultures often lead to flattening of identities, wherein the model resorts to providing suggestions from the dominant high-resource cultures. this has substantial implications for future work on how llms should adapt to output based on user names and assumed culture, presenting a complex interplay between beneficial customisation and the inadvertent reinforcement of biases, and requiring fundamental and nuanced considerations. bias in llms names are deeply intertwined with personal and cultural identity @xcite . tajfel (2010)'s social identity theory posits that individuals derive a significant part of their self-concept from their membership in social groups, with names acting as identifiers of these affiliations. however, there can be a disconnect between one's name and cultural background, leading to complex implications for one's sense of belonging @xcite . as names can lead to simplified presumptions about user identity, names have been used across a variety of studies investigating bias in llms. for example, @xcite prompts llms with scenarios involving individuals with names associated with various racial and gender groups in the american cultural context. their findings reveal that the models systematically disadvantage names commonly linked to racial minorities and women. @xcite and @xcite highlight name-driven biases affecting interpersonal trust predictions and hiring-related communications, respectively. names have been used as a proxy to study gender @xcite @xcite and ethnic identity bias @xcite . there has been a recent increase in work on cultural biases in llms @xcite . the closest to our work is work done by openai (2024a) evaluating the bias introduced by names in chatgpt. they state that users often share their own names with chat assistants for tasks such as writing e-mails. while their work focuses on the propagation of harmful stereotypes related to race and gender, our study focuses on broader cultural presumptions based on names. ## methodology we outline our experimental setup in figure 2 we generate responses to different questions usfor more details of the methodology, refer to section 3 ing prompts with and without names in them. we then assess bias in responses in the form of cultural presumptions through two methodologies and compare their performance. the details of various parts of our pipeline are as follows. ## analyzing cultural identity flattening not all names elicit biased responses from the models. in fact, the distribution is quite skewed (figure 10 in the appendix). the distribution of biased responses per name is heavily skewed, with most names having relatively few biased responses and a smaller subset having substantially higher counts. ## discussion through our experiments, we demonstrate that llms implicitly personalize their responses by inferring user background from names. on one hand, tailoring advice-such as suggesting sushi to a 'takashi' or tacos to a 'maría'-can feel like welcome personalization when it aligns with user expectations @xcite ) -these surface associations risk cementing narrow cultural tropes and 'flattening' the multifaceted identities that names actually represent @xcite . the implicit personalization of responses by inferring user background from names creates 'personalization-stereotype paradox' @xcite . what might seem like helpful customization, such as suggesting culturally relevant cuisine, can simultaneously flatten rich cultural identities into monolithic stereotypes, reflecting inherent trade-offs between fairness and accuracy in algorithmic systems @xcite . such customizations create an 'illusion of personalization' where users perceive tailored responses as genuine understanding, potentially masking underlying stereotypes and increasing trust in biased information @xcite . this can lead to stereotypical, irrelevant, or offensive user experiences, making individuals from less-represented cultures feel misunderstood or marginalized. relying solely on names to determine cultural identity is problematic, as names can be unreliable proxies for complex, often multicultural, identities @xcite . naming conventions are further complicated by cultural assimilation, resistance to convention, and increasingly heterogeneous so-cieties where cross-cultural associations differ significantly @xcite . this attempt to infer a singular cultural identity often leads to the oversimplification of complex identities. for example, for individuals from diaspora communities or with multicultural backgrounds, this constant algorithmic flattening can act as a persistent microaggression, invalidating their complex identities @xcite . more broadly, this reinforces a simplistic and often stereotypical worldview for all users, mirroring concerns that llms act as "stochastic parrots" that mindlessly repeat biases from their training data without any true understanding @xcite . this effect is amplified by the commercial incentives of many ai platforms, which can prioritize engagement through simplistic stereotypes, echoing patterns seen in how search engines have been shown to reinforce racism @xcite . ultimately, these broader harms manifest in tangible ways. when llms provide culturally biased information based on names, it can negatively affect users' access to diverse perspectives, particularly in culturally significant domains like food, clothing, and traditions @xcite . this lack of access can manifest as disparate treatment, where names commonly associated with racial minorities receive systematically different, often less advantageous, outputs from llms @xcite . the name-trait correlations are further exacerbated by biases in pre-training corpora, where certain names disproportionately co-occur with culturally loaded contexts, thereby embedding skewed associations into llm representations @xcite @xcite . our findings underscore the need for more nuanced personalization systems that avoid reinforcing stereotypes while maintaining meaningful customization, acknowledging the difficult tradeoffs involved in achieving both fairness and useful personalization. this is especially pressing considering the anthropomorphic and relationshipbuilding behaviour that models are trained to interact with @xcite . above all, it is crucial for models to be trained to be transparent in the presumptions they are making and to convey the implicit personalisation taking place. ## conclusion our study provides a framework for establishing and measuring personalisation in llms based on a cultural presumption made from a user's name. we find strong evidence of cultural identity presumptions, particularly for names from east asian, russian, and indian cultures, while names from ireland, brazil, and the philippines lead to more diverse and generic responses. furthermore, a facetbased analysis indicates that clothing and tradition queries amplify bias most dramatically, especially when key terms such as 'tradition' are present. our cross-cultural analysis highlights the issue of cultural flattening -that models consistently favour some cultures over others when names are common across cultures. we hope this study will serve as a useful reference for considerations on the utility vs. harms of names-based personalisation of llms.