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 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.
The paper presents an expert-driven method for turning normative principles—specifically Islamic ethical, theological, and jurisprudential traditions—into alignment data for language models. Over a year, seven experts curated 2.8K supervised fine-tuning examples and 5.4K preference pairs in Arabic-English, then evaluated models trained on these datasets. Experiments show that models trained with the curated SFT data outperform a baseline in expert judgments, while adding preference data yields a smaller, non-significant improvement.
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.
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.
The study examines how large language models (ChatGPT, Gemini, and Grok) embed religious bias in AI‑generated financial advice. Using 432 simulated advisor‑client interactions across four religious identities and three financial decisions, the authors find that only 12‑18% of advice is unbiased, with Gemini showing the most bias and ChatGPT comparable to Grok. The research identifies structural biases in model design and discursive mechanisms—such as religious anchoring and tone modulation—that vary by scenario, revealing a tension between personalization and neutrality in AI advisory services.
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:2607. 19856v1 Announce Type: cross Abstract: FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi.
The IGT system tackles PolyFiQA Task 2 of the FinMMEval Lab, a multilingual financial QA challenge involving English SEC filings and news in five languages. It distinguishes two question families: numeric‑structured queries are answered via keyword extraction from filings, while synthesis queries use rule‑based passage selection from news. The approach yields a development ROUGE‑1 of ~0.395, a 60% boost over a generic RAG baseline, and places third among twelve teams on the official test set.
arXiv:2608. 07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern.
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:2603. 16827v2 Announce Type: replace Abstract: Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations.