arXiv Computation and Language By Nawar S. Alseelawi, Mustafa S. Aljumaily

Qistas: A Pre-Registered, Portable Protocol for Fairness Evaluation of Large Language Models

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI
Sep 1

Beyond Fluency: A Rubric-Based Benchmark for Evaluating Saudi Dialect and Cultural Competence in Large Language Models

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.

By Ghassan Al-Sumaidaee, Sajjad Abdoli, Ahmed Rashad, Maxim Legg
arXiv Computation and Language
Oct 23

Harm Is Behavioural, Moderation Is Lexical: How Language and Culture Decide Whom Automated Safety Systems Protect and Whom They Silence

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.

By Gabriel Lars Sabadin, Dr Nicola Harding, Sarah Kingston