# Presumed Cultural Identity: How Names Shape LLM Responses

arXiv: https://arxiv.org/abs/2502.11995 · HTML: https://mlsanity.com/paper/2502.11995/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 80 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 29 of 29)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [Large language models that replace human participants can harmfully misportray and flatten identity groups](https://mlsanity.com/paper/2402.01908/) (2024)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting](https://mlsanity.com/paper/2404.10199/) (2024)
- [What's in a Name? Auditing Large Language Models for Race and Gender Bias](https://mlsanity.com/paper/2402.14875/) (2024)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023)
- ["You are grounded!": Latent Name Artifacts in Pre-trained Language Models](https://mlsanity.com/paper/2004.03012/) (2020)
- [Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models](https://mlsanity.com/paper/2110.00672/) (2021)
- [Stereotype or Personalization? User Identity Biases Chatbot Recommendations](https://mlsanity.com/paper/2410.05613/) (2024)
- ["You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations](https://mlsanity.com/paper/2406.12232/) (2024)
- [Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models](https://mlsanity.com/paper/2109.03300/) (2021)
- [On the Way to LLM Personalization: Learning to Remember User Conversations](https://mlsanity.com/paper/2411.13405/) (2024)
- [Implicit Personalization in Language Models: A Systematic Study](https://mlsanity.com/paper/2405.14808/) (2024)
- [Exploring Changes in Nation Perception with Nationality-Assigned Personas in LLMs](https://mlsanity.com/paper/2406.13993/) (2024)
- [Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game](https://mlsanity.com/paper/2404.14682/) (2024)
- [Enriching Datasets with Demographics through Large Language Models: What's in a Name?](https://mlsanity.com/paper/2409.11491/) (2024)
- [How Personal is Machine Learning Personalization?](https://mlsanity.com/paper/1912.07938/) (2019)

## Built on by (most cited first, 21 of 21)

- [The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models](https://mlsanity.com/paper/2507.16076/) (2025)
- [Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization](https://mlsanity.com/paper/2505.16467/) (2025)
- [One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization](https://mlsanity.com/paper/2601.18572/) (2026)
- [The Inadequacy of Offline LLM Evaluations: A Need to Account for Personalization in Model Behavior](https://mlsanity.com/paper/2509.19364/) (2025)
- [Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias](https://mlsanity.com/paper/2601.18486/) (2026)
- [Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks](https://mlsanity.com/paper/2601.22396/) (2026)
- [Cultural Value Alignment Via Latent Activation Steering in Large Language Models](https://mlsanity.com/paper/2605.26365/) (2026)
- [Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages](https://mlsanity.com/paper/2510.05291/) (2025)
- [Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers](https://mlsanity.com/paper/2606.02776/) (2026)
- [Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Version](https://mlsanity.com/paper/2606.11399/) (2026)
- [Robust Native Language Identification through Agentic Decomposition](https://mlsanity.com/paper/2509.16666/) (2025)
- [Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation](https://mlsanity.com/paper/2605.12515/) (2026)
- [AmchiBias: Measuring Stereotypical Bias in Goan Identity Groups with a Minimal Pair Dataset in English and Konkani](https://mlsanity.com/paper/2606.15191/) (2026)
- [Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maxims](https://mlsanity.com/paper/2606.02493/) (2026)
- [Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Research](https://mlsanity.com/paper/2606.12247/) (2026)
- [CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries](https://mlsanity.com/paper/2607.05405/) (2026)
- [Status Association Does Not Reliably Predict Decision Leakage](https://mlsanity.com/paper/2608.10089/) (2026)
- [No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluation](https://mlsanity.com/paper/2608.21206/) (2026)
- [Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence](https://mlsanity.com/paper/2609.19965/) (2026)
- [Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making](https://mlsanity.com/paper/2609.22225/) (2026)
- [Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models](https://mlsanity.com/paper/2609.34065/) (2026)

## Abstract (arXiv)

Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, using names as a core indicator of identity can lead to over-simplification of complex identities. When interacting with LLMs, user names are an important point of information for personalisation. 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. We study biases associated with names by measuring cultural presumptions in the responses generated by LLMs when presented with common suggestion-seeking queries, which might involve making assumptions about the user. Our analyses demonstrate strong assumptions about cultural identity associated with names present in LLM generations across multiple cultures. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.