arXiv:2608.29378v1 Announce Type: cross
Abstract: Arabic large language models must refuse harmful prompts without over-refusing benign or sensitive prompts, yet a single refusal rate hides this trad...
By Mohamad Zbib, Ammar Mohanna
arXiv:2606. 01322v1 Announce Type: cross Abstract: Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones, critically underexplored.
By Victor Akinode, Senyu Li, Wassim Hamidouche, Waqas Zamir, Inbal Becker-Reshef, David Ifeoluwa Adelani
The paper demonstrates that large language model (LLM) evaluators, whether reward‑model based or prompted LLM‑as‑a‑Judge, exhibit significant language bias in multilingual settings. Experiments with semantically identical instruction‑response pairs across 23 languages reveal that lower‑resource languages receive higher scores, a bias that persists across eight open‑weight evaluators and is not detectable by standard pairwise accuracy metrics. The authors link the bias to model uncertainty and language identity, showing it cannot be explained by content difficulty alone.
By Ej Zhou, Lucas Resck, Zheng Hui, Anna Korhonen
arXiv:2606. 01196v1 Announce Type: cross Abstract: Safety alignment learned in high-resource languages transfers poorly to low-resource languages.
By Rashad Aziz, Ikhlasul Akmal Hanif, Fajri Koto
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...
By Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh, Ihsen Alouani, Mohammed E. Fouda
arXiv:2605. 17173v2 Announce Type: replace-cross Abstract: Large language models exhibit safety degradation in non-English languages.
By Max Zhang, Ameen Patel, Sang T. Truong, Sanmi Koyejo