We are sharing what we learned from our conversations across 22 countries, and how we will be incorporating those insights moving forward.
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New OpenAI Signals data shows how people use ChatGPT worldwide, with country-level insights on adoption, usage trends, and evolving behavior.
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.
arXiv:2609.00491v1 Announce Type: new Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
The paper investigates how post‑training fine‑tuning of large language models can reduce their ability to adapt to in‑context information, particularly when the models are fine‑tuned toward one side of cultural‑value disagreements. Experiments show that as a model is trained to favor a specific perspective, its capacity to recognize and enact opposing viewpoints diminishes over time. The authors propose an alternative objective that balances reward maximization with a prescribed distribution over expressed perspectives, offering a practical stance‑distribution matching implementation.
arXiv:2607. 05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.
Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely.