The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology. Large Language Models (LLMs), promoted as repositories of collective human knowledge, raise a pivotal question: do these models genuinely encapsulate the diverse knowledge adopted by different cultures? Our study reveals that these models demonstrate greater cultural alignment along two dimensions -- firstly, when prompted with the dominant language of a specific culture, and secondly, when pretrained with a refined mixture of languages employed by that culture. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. Specifically, we replicate a survey conducted in various regions of Egypt and the United States through prompting LLMs with different pretraining data mixtures in both Arabic and English with the personas of the real respondents and the survey questions. Further analysis reveals that misalignment becomes more pronounced for underrepresented personas and for culturally sensitive topics, such as those probing social values. Finally, we introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment. Our study emphasizes the necessity for a more balanced multilingual pretraining dataset to better represent the diversity of human experience and the plurality of different cultures with many implications on the topic of cross-lingual transfer.

We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams.

Various countries across the Middle East have placed an emphasis on AI. Clear examples of that have been nation-wide strategies around AI and part of wider government digital transformations. In the Gulf Cooperation Council (GCC) region (Saudi Arabia, Qatar, Oman, Bahrain, Kuwait and the United Arab Emirates (UAE), economic development diversifications such as Saudi Vision 2030 has prioritised wider future and innovative economies. Sectors such as fintech and AI-both fintech and non-fintech related- play a strong role in that. It is not just nation-wide economic development diversification strategies such as Vision 2030 but also, complimenting and in parallel, strategies purely around AI.

Interest in artificial intelligence is on the rise in Egypt as enterprises embrace emerging technology to expand into new markets, investors back AI startups and government initiatives support education and awareness of the technology. There is mounting evidence that private enterprise is embracing AI. Recently, for example, AI and anlytics vendor fonYou partnered with a mobile operator in Egypt to use its AI module to reach the unbanked, and Widebot just raised a six-figure (USD) Pre-Series A investment for its Arabic language chatbot. Meanwhile, the government is looking to develop AI capabilities in a number of ways, including launching its first AI faculty at Kafr El Sheikh University. Egypt is aiming to have 7.7 percent of its GDP derived through AI by 2030, a figure touted in the PricewaterhouseCoopers (PwC) report, The Potential Impact of AI in the Middle East.