Multilingual benchmarks for African languages mostly translate generic questions into local languages, measuring linguistic competence but not whether models know facts about Africa. We introduce \nai...
arXiv:2609.36205v1 Announce Type: new
Abstract: We study how Gemma 4 31B represents African languages and responds to cultural steering. The first study compares nine African languages and three cont...
By Muhammad Abdullahi Said, Jonathan Shock
arXiv:2606. 07422v1 Announce Type: cross Abstract: Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural knowledge is better accessed through English or the local language.
By Yang Zhang, Xiao Fei, Amr Mohamed, Sarah Almeida Carneiro, Mersin Konomi, Mingmeng Geng, Ahmed Asaad, Guokan Shang, Michalis Vazirgiannis
The paper surveys NLP research on Nigeria’s three major low‑resource languages—Hausa, Yoruba, and Igbo—covering over 500 languages spoken by 175 million people. It reviews 293 studies, finding that only 27.6% produced new linguistic resources, indicating a heavy reliance on repurposing existing data. The authors highlight under‑explored challenges such as morphological analysis and diacritic representation, and call for collaborative resource enrichment and community support to advance NaijaNLP and low‑resource NLP more broadly.
By Isa Inuwa-Dutse
The paper introduces TriviaRoomQA, a multilingual benchmark of 3,300 parallel multiple-choice questions across 288 topics, plus an additional 5,340 French-only questions. It evaluates 30 open-weight large language models (7–70 B parameters) from various regions, finding strong performance on knowledge-intensive subjects like history and geography but weaker results on everyday popular‑culture topics such as celebrities, music, movies, and news. The study also shows that model performance varies across languages for the same questions, indicating that factual knowledge access is not always language‑independent.
By Anna Mosolova, Djam\'e Seddah
Quiz rooms, trivia nights, and quiz shows challenge human knowledge across a wide range of topics, from canonical facts to everyday culture. In this paper, we examine whether large language models (LLMs) can perform competitively in such settings, using quiz-style questions to test them on both common and niche topics.