The study audits the cultural values expressed by the decision‑only language model JEV using the 2013 Values Survey Module. By presenting 24 items to JEV under 12 matched Saudi and 12 matched American personas, in both English and Arabic, and across eight request formulations, the researchers found that JEV’s responses were highly repeatable (ICC 0.997) and that persona and language significantly influenced the model’s value profiles. Saudi personas shifted JEV’s answers toward the human Saudi‑US difference—capturing 87 % of the effect in English and 62 % in Arabic—while language, age, and gender also modulated the outcomes.
arXiv:2609.16006v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test wha...
arXiv:2607. 24782v1 Announce Type: new Abstract: LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would answer value-laden questions.
arXiv:2601. 22396v2 Announce Type: replace-cross Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain.
The paper introduces a rubric-based benchmark to evaluate Saudi Arabic dialect and cultural competence in large language models. It comprises 31 expert-authored prompts covering idiomatic, pragmatic, lexical, and culturally embedded aspects, each paired with an expert-established ground truth. Four state-of-the-art models were scored, revealing that none exceeded 55% accuracy and that ambiguous framing was the most common error type.
arXiv:2606. 12443v1 Announce Type: cross Abstract: Social roles shape expectations, priorities, and judgments, yet it remains unclear how large language models (LLMs) associate occupational identities with broader cultural value patterns.
The paper introduces the Middle East Cultural Sensitivity Score (MECSS) to quantify Orientalist bias in large language models, converting Said’s seven Orientalist operations into measurable dimensions. Using 280 conversations, it finds that GPT‑4 and Falcon3‑7B‑Instruct systematically reproduce Orientalist patterns, with Falcon scoring higher despite being regionally built. The study highlights that geographic origin alone does not mitigate bias and identifies a new failure mode, "Said‑washing," present in 87.9% of GPT‑4 interactions.
arXiv:2608.29266v1 Announce Type: cross Abstract: Researchers increasingly treat LLM survey responses as a proxy for human cultural values. This includes projecting model outputs onto instruments lik...
The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.
The paper introduces Cultural Divergence Preservation (CDP), a new diagnostic for evaluating whether large language models (LLMs) preserve cross‑country differences when used as synthetic survey respondents. CDP uses a single human calibration to detect cultural flattening (reduced divergence) or caricature (increased divergence) and is shown to vary monotonically with cross‑country divergence, unlike conventional Jensen–Shannon divergence metrics. Experiments across multiple LLM backbones, prompting methods, and survey domains reveal that CDP uncovers systematic discrepancies with traditional fidelity metrics, highlighting that methods favored by those metrics can still produce strong flattening.
arXiv:2607. 05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.
The paper "Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems" investigates how commercial text‑to‑image models silently modify user prompts before generating images, a step that is often hidden from users. Using the multilingual benchmark WORLDVIEW, the authors audit the revision layer in DALL‑E‑3, Imagen‑4, and GPT‑Image‑1.5, finding that non‑Western and non‑Anglophone contexts are disproportionately marked, reduced to narrow vocabularies, and stereotyped. The study demonstrates that the revision layer itself is a previously undocumented causal source of cultural stereotyping, underscoring the need to audit deployed systems rather than just the underlying models.
Camellia is a new benchmark that tests cultural bias in large language models (LLMs) across nine Asian languages and six Asian cultures. It contains 19,530 manually annotated entities linked to Asian or Western cultures and 2,173 masked social‑media contexts for these entities. Using Camellia, the authors evaluate four multilingual LLMs on cultural context adaptation, sentiment association, and entity extractive QA, finding that models struggle with cultural adaptation, exhibit differing biases across regions and families, and have difficulty understanding context in some Asian languages.