mlsanityData to 14 Oct 2026

Presumed Cultural Identity: How Names Shape LLM Responses

arXiv 2502.11995 ↗First posted 17 Feb 2025cs.CL cs.AIcited by 21

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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.

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

29 of 80 references.

References · 29

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  4. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  6. 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
  7. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  8. 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
  9. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 199
  10. 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 199
  11. 2024Large language models that replace human participants can harmfully misportray and flatten identity groupscited by 144
  12. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
17 more
  1. 2018The Geometry of Culture: Analyzing Meaning through Word Embeddingscited by 87
  2. 2022Extracting Cultural Commonsense Knowledge at Scalecited by 76
  3. 2024CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Promptingcited by 55
  4. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 51
  5. 2025Why human-AI relationships need socioaffective alignmentcited by 47
  6. 2023The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactionscited by 45
  7. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39
  8. 2021Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Modelscited by 39
  9. 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 34
  10. 2024"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendationscited by 33
  11. 2021Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelscited by 23
  12. 2024On the Way to LLM Personalization: Learning to Remember User Conversationscited by 21
  13. 2024Implicit Personalization in Language Models: A Systematic Studycited by 18
  14. 2024Exploring Changes in Nation Perception with Nationality-Assigned Personas in LLMscited by 11
  15. 2024Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Gamecited by 3
  16. 2024Enriching Datasets with Demographics through Large Language Models: What's in a Name?cited by 3
  17. 2019How Personal is Machine Learning Personalization?cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 16.

21 papers.

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  1. 2025The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Modelscited by 42
  2. 2025Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalizationcited by 24
  3. 2026One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalizationcited by 9
  4. 2025The Inadequacy of Offline LLM Evaluations: A Need to Account for Personalization in Model Behaviorcited by 8
  5. 2026Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Biascited by 4
  6. 2026Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworkscited by 3
  7. 2026Cultural Value Alignment Via Latent Activation Steering in Large Language Modelscited by 3
  8. 2025Camellia: Benchmarking Cultural Biases in LLMs for Asian Languagescited by 2
  9. 2026Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answerscited by 2
  10. 2026Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Versioncited by 2
  11. 2025Robust Native Language Identification through Agentic Decompositioncited by 1
  12. 2026Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisationcited by 1
9 more
  1. 2026AmchiBias: Measuring Stereotypical Bias in Goan Identity Groups with a Minimal Pair Dataset in English and Konkanicited by 1
  2. 2026Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maximscited by 0
  3. 2026Beyond Third-Person Audits: Situated Interaction Auditing for User-Centered LLM Bias Researchcited by 0
  4. 2026CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queriescited by 0
  5. 2026Status Association Does Not Reliably Predict Decision Leakagecited by 0
  6. 2026No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluationcited by 0
  7. 2026Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidencecited by 0
  8. 2026Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Makingcited by 0
  9. 2026Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Modelscited by 0