| Challenge: | Names can be used as markers of individuality, cultural heritage, and personal history when interacting with chatbots. |
| Approach: | They propose to use names as cultural bias in chatbots to adapt to user input and task contexts. |
| Outcome: | The proposed method demonstrates that LLMs make cultural identity assumptions based on their users’ presumed backgrounds based upon their names . |
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| Challenge: | Prior work has shown that such inferences can lead to lower quality responses for users assumed to be from minority groups. |
| Approach: | They analyze LLMs' latent user representations through both model internals and generated answers to targeted user questions. |
| Outcome: | The proposed models infer demographic attributes based on stereotypical signals, which persists even when the user explicitly identifies with a different demographic group. |
Stereotype or Personalization? User Identity Biases Chatbot Recommendations (2025.findings-acl)
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| Challenge: | We show that when people use large language models to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is. |
| Approach: | They propose that chatbots should transparently indicate when user’s revealed identity influences model recommendations but fail to do so . |
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One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization (2026.acl-long)
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| Challenge: | Prior work has used personas to study biases by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. |
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Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment (2025.findings-emnlp)
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| Challenge: | Recent studies have explored personality evaluation of LLMs, but they largely overlook the interplay between culture and personality. |
| Approach: | They propose a large-scale benchmark for evaluating LLMs’ personality expression in culturally grounded, behaviorally rich contexts. |
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Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas (2024.findings-emnlp)
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Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle Ungar, Brenda Curtis
| Challenge: | Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. |
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Ask LLMs Directly, “What shapes your bias?”: Measuring Social Bias in Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods to evaluate social bias in large language models have limitations . et al., 1995: stereotypes shape social perceptions without objective basis . |
| Approach: | They propose a method to intuitively quantify social perceptions and suggest metrics to evaluate biases within LLMs. |
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Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text (2025.findings-emnlp)
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| Challenge: | Persona-prompting is a growing strategy to personalize outputs, but its impact on how LLMs represent social groups remains underexplored. |
| Approach: | They investigate whether persona-prompting leads to different levels of linguistic abstraction . they compare 11 persona driven responses to those of a generic AI assistant . |
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Towards Measuring and Modeling “Culture” in LLMs: A Survey (2024.emnlp-main)
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Muhammad Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Aji, Jacki O’Neill, Ashutosh Modi, Monojit Choudhury
| Challenge: | Existing models are biased towards Western, Anglocentric or American cultures, a problem that is arguably detrimental to the performance of LLMs. |
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Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek (2026.acl-srw)
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| Challenge: | a recent study found that large language models are biased overwhelmingly Anglocentric . a stereotype perceptual map is a framework for analyzing how ethnic groups are positioned along evaluative dimensions. |
| Approach: | They use a stereotype perceptual map to examine how ethnic groups are positioned along evaluative dimensions. |
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Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4 (2024.naacl-srw)
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| Challenge: | Large Language Models (LLMs) have been criticized for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes. |
| Approach: | They devised a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences. |
| Outcome: | The proposed model produces twice the level of toxic content as ChatGPT and GPT-4 under certain conditions. |