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: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...
The paper investigates how large language models (LLMs) represent nine transborder ethnic communities along China’s southwestern border with mainland Southeast Asia, such as the Sami, Rohingya, and Kurds. It introduces the concept of identity ossification, where LLMs reduce fluid, cross-border identities to a single nation-state narrative, and presents the Transborder Identity Ossification Diagnostic Framework (TIODF) to differentiate between knowledge gaps and narrative framing. Experiments with GPT‑5.1 and DeepSeek‑V3.2 in English and Chinese show that ossification rates vary widely, with DeepSeek‑V3.2’s Chinese responses exhibiting the phenomenon in 62.6% of cases, indicating that narrative framing rather than mere knowledge absence drives the issue.
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. 01260v1 Announce Type: cross Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia, thus leaving a critical gap in evaluating representational fairness and localized stereotypes within its uniquely vast, multilingual, and diverse sociocultural landscape.
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
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.
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
arXiv:2606. 28333v1 Announce Type: cross Abstract: \begin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.
arXiv:2608. 11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined.
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:2606. 26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations.
arXiv:2609.00999v1 Announce Type: cross Abstract: When a question has valid answers under different normative frameworks, a language model must decide which framework to use and whether it can answer...