arXiv:2608. 11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined.
By Guang Yang, Fengchen Liu, Alex Wang, Homa Hosseinmardi, Amir Ghasemian
arXiv:2610.19848v1 Announce Type: new
Abstract: We present OpenTWBench, an open evaluation suite for Taiwan-domain knowledge in large language models, built around two benchmarks. tw-exam-bench conta...
By Liang-Hsun Huang, Min-Yi Chen, Chao-Chun Chuang, Yu-Wei Liu, Che-Yu Wu, Po-Feng Lee
arXiv:2603.18280v4 Announce Type: replace-cross
Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the...
By Gregory N. Frank
arXiv:2611.03171v1 Announce Type: cross
Abstract: On July 1, 2025, Vietnam reduced its 63 provinces to 34, abolished its 696 districts, and replaced its 10,035 commune-level units (wards) with 3,321....
By Phan Binh Nguyen Lam, Nguyen Thai Anh
The paper introduces a method for measuring political positions in data‑sparse regions by treating a large language model as a single fallible rater within a panel, analogous to expert surveys. It demonstrates that this panel approach yields reliable agreement across multiple models and reveals that disagreements often stem from differing interpretations of the regime rather than factual errors. The authors provide a full rubric, prompt, and data set, using the Middle East and North Africa as a worked example.
By Tarek Gara
The paper "Benchmarking LLM Compliance with China AI Generated Content Regulations" presents a new framework for evaluating large language models (LLMs) against China’s AI‑generated content compliance rules. Using 2,303 questions across six dimensions—including 203 self‑constructed constitutional queries—the authors assess 20 prominent LLMs, finding that international models show high compliance, especially on ideologically aligned dimensions. The study establishes a regulatory benchmark that allows the global AI community to test both Chinese and non‑Chinese LLMs under unified, legally grounded compliance criteria.
By Chenrui Cui, Hongye Fang, Lisha Song, Weichao Chen, Yue Zhu, Gang Xu