arXiv Computation and Language
4d ago

Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs

The paper investigates cultural biases in large language models (LLMs) by introducing the Culture-Related Open Questions (CROQ) dataset, which contains 24‑language questions about generic culture. Experiments reveal that LLMs disproportionately favor Japan in their responses, especially when prompted in high‑resource languages, while low‑resource languages tend to highlight countries where the language is official. The study also finds that these biases emerge after supervised fine‑tuning rather than during pre‑training.

By Joseba Fernandez de Landa, Carla Perez-Almendros, Jose Camacho-Collados
arXiv AI
1d ago

Accurate in space, unreliable in time: how LLMs represent national cultural change

The paper investigates how large language models (LLMs) represent national cultural change over time, using more than two decades of World Values Survey data and the Inglehart‑Welzel cultural map. It finds that while LLMs generally place countries near their most recent surveyed positions, their representations lag behind current data, under‑capture the magnitude of change, introduce spurious movements, and rarely reproduce trajectory reversals. These temporal inaccuracies reveal a flattening effect that limits the models’ cultural awareness and raises concerns for evaluation, representational harms, and governance of culturally aware AI systems.

By Yalda Daryani, Miranda Bogen, Madeleine I. G. Daepp
arXiv AI
Jul 24

Response drift across frontier large language models

arXiv:2607. 20454v1 Announce Type: cross Abstract: All frontier large language models (LLMs) exhibit response drift -- producing outputs that deviate from expert-validated references -- yet the magnitude and structure of this drift remain uncharacterised by systematic human evaluation.

By Mohammed Aledhari, Ali Aledhari, Fatimah Aledhari, Gowtham Venkat Eathamokkala, Mohamed Rahouti