arXiv Computation and Language
2d ago

From Language to Country to Valence: A Multilingual Audit of Stereotype Bias Across LLMs

The paper presents a multilingual audit of stereotype bias in large language models (LLMs) using 12,000 locally written Reddit posts in English, Spanish, French, and German. It evaluates gender, racial, and religious bias across six models (three open-weight and three proprietary APIs) with a two-blank design to reduce grammatical cues, finding that religion shows the highest imbalance, Spanish leads in gender bias, and German in race and religion. The study highlights that aggregate scores can mask category- and context-specific biases, underscoring the need for detailed inspection before deploying LLMs in consequential settings.

By Zihao Sun
arXiv AI
Sep 16

Beyond the Name: Demographic Leakage in De-Identified R\'esum\'es and Evaluation Artifacts in LLM Bias Audits

The paper examines whether removing declared language fields from de‑identified résumés eliminates demographic leakage in large language models. By keeping language attributes identical and varying only unstructured prose across five ethnocultural groups and three cue‑salience levels, the authors find that non‑language text still allows target‑group recovery (average 0.757, reaching 1.000 under high salience). They also show that evaluation design—such as allowing or forbidding ties—dramatically affects LLM‑as‑a‑judge outcomes, underscoring the importance of evaluation protocol in bias audits.

By Qiangju Chen, Yang Xiao
arXiv AI
Jul 15

Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)

arXiv:2607. 12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation.

By Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain