Presumed Cultural Identity: How Names Shape LLM Responses
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.
67b65bbe0d878eff1a6b111d
Siddhesh Pawar
admin_assigned
2025-02-20T16:11:39.727Z
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Siddhesh Pawar
user
Siddheshmp06
67b65bbe0d878eff1a6b111e
Arnav Arora
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Lucie-Aimée Kaffee
claimed_verified
2025-02-20T15:53:09.702Z
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Lucie-Aimée Kaffee
user
frimelle
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Isabelle Augenstein
claimed_verified
2025-02-20T09:36:32.278Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1621507769190-608918b7df398c3b285ce960.jpeg
Isabelle Augenstein
user
IAugenstein
Presumed Cultural Identity: How Names Shape LLM Responses
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.
LLMs make strong cultural assumptions based on names during personalization, leading to biases in responses, and this study explores ways to improve personalization systems without reinforcing stereotypes.
LLMs cultural presumptions suggestion-seeking queries cultural identity personalization systems stereotypes