The National ·
The article discusses the rise of large language models like ChatGPT and Gemini. It highlights their role in driving the first wave of AI development. Why it matters: While lacking specifics, the article suggests ongoing interest in the impact and future of LLMs, a key area of AI research and development.
arXiv ·
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
MBZUAI ·
Ted Briscoe from the University of Cambridge discussed using machine learning and NLP to develop learning-oriented assessment (LOA) for non-native writers. The technology is used in Cambridge English courseware like Empower and Linguaskill, as well as Write and Improve. Briscoe is also the co-founder and CEO of iLexIR Ltd. Why it matters: Improving automated language assessment could significantly enhance online language learning platforms in the Arab world and beyond.
arXiv ·
Researchers at the American University of Beirut (AUB) have released AraBERT, a BERT model pre-trained specifically for Arabic language understanding. The model was trained on a large Arabic corpus and compared against multilingual BERT and other state-of-the-art methods. AraBERT achieved state-of-the-art performance on several tested Arabic NLP tasks including sentiment analysis, named entity recognition, and question answering. Why it matters: This release provides the Arabic NLP community with a high-performing, open-source language model, facilitating further research and development.
arXiv ·
The paper introduces SectEval, a new benchmark to evaluate sectarian biases in LLMs concerning Sunni and Shia Islam, available in English and Hindi. Results show significant inconsistencies in LLM responses based on language, with some models favoring Shia responses in English but Sunni in Hindi. Location-based experiments further reveal that advanced models adapt their responses based on the user's claimed country, while smaller models exhibit a consistent Sunni-leaning bias.
arXiv ·
The GenAI Content Detection Task 1 is a shared task on detecting machine-generated text, featuring monolingual (English) and multilingual subtasks. The task, part of the GenAI workshop at COLING 2025, attracted 36 teams for the English subtask and 26 for the multilingual one. The organizers provide a detailed overview of the data, results, system rankings, and analysis of the submitted systems.