Qistas is a new protocol designed to evaluate how large language models treat the social, confessional, and regional constituencies of a society in a portable, pre‑registered manner. The pilot implementation focuses on Iraq and measures procedural symmetry, differential refusal, stereotype reliance, and terminological framing using paired statistical tests and a power analysis that specifies detectable effect sizes before data collection. The authors release an authoring apparatus—including a taxonomy, checklists, audit logs, and a human review notebook—and report a field test that uncovered several defect patterns common to counterfactual fairness benchmarks.
arXiv:2609.13980v1 Announce Type: cross Abstract: Arabic large-language-model (LLM) evaluation has matured around Modern Standard Arabic (MSA): aggregated leaderboards such as the Open Arabic LLM Lea...
arXiv:2610.41486v1 Announce Type: new Abstract: While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relatio...
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: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:2605. 25420v2 Announce Type: replace-cross Abstract: Large language model safety evaluation remains heavily English-centered, leaving low-resource languages under-measured even when models are deployed globally.
The paper examines how automated safety systems unevenly flag harmful content, disproportionately labeling African American English as offensive and misclassifying content from drag queens and white nationalists. It identifies seven failure modes rooted in vocabulary-based harm detection and proposes an evaluation protocol that highlights gaps in performance across languages. Applied to five open systems in eleven languages, the study finds that none achieve native-speaker accuracy, and higher capability often increases discrimination, widening the disparity between best- and worst-served language communities.
A new large-scale Arabic fact‑checking dataset called Arafa has been created using an automated pipeline that generates claims from Arabic Wikipedia, mutates them into counterfactuals, and validates them against supporting or refuting evidence. The dataset contains 181,976 claim‑evidence pairs labeled as supported, refuted, or not enough information, and human evaluation shows high inter‑annotator agreement and strong validation accuracy. Fine‑tuned transformer models on Arafa achieve a Macro F1‑score of 77%, demonstrating its usefulness for Arabic fact‑checking tasks.
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:2611.02491v1 Announce Type: new Abstract: Large language models are increasingly used as measurement instruments for socially consequential constructs such as hate speech, and are typically val...
BanglaBBQ is a question‑answering bias benchmark that captures Bangladeshi stereotypes in both Bangla and English. It adapts the original BBQ by classifying templates into directly translated, modified for Bangladesh, and removed categories, retaining 140 templates across six groups. Five new stereotype categories—marital status, age and political perception, regional origin, educational background, and mental‑health status—were added, with 466 new templates evaluated and 372 retained after annotation. The dataset was tested on Qwen3‑4B, Qwen3‑8B, GPT‑4.1‑mini, and Gemini 2.5 Flash, revealing that all models tend to favor stereotyped individuals when context is ambiguous, with language‑specific differences and varying accuracy depending on stereotype confirmation or contradiction.
arXiv:2603.04410v3 Announce Type: replace-cross Abstract: While different stakeholders are trying to leverage Arabic Language Models (ALMs), safety alignment in ALMs remains largely underexplored, hi...
The paper investigates whether lower stereotype bias scores in multilingual language models truly indicate greater fairness. By auditing ten open models across eight languages, the authors show that lower average scores often arise from translation artifacts rather than genuine bias reduction, as item‑level correlations between English and translated scores are weak. They also identify pipeline errors that can mask bias signals and provide code to replicate their findings.