NaijaFacts is a multilingual benchmark that tests language models on factual and statistical questions about Nigeria, presented in English, Nigerian Pidgin, Yoruba, Igbo, and Hausa. The study shows that models perform much better on UK and US facts (49–50% accuracy) than on Nigerian facts (20% accuracy), indicating a strong knowledge gap. Language effects are smaller, with performance dropping from English to Yoruba, but overall scale does not improve accuracy on Nigerian statistics.
By Zainab Afolabi, Nikita Kozodoi, Jack Butler, Babs Khalidson
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
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
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
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.
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
AfrIFact is a new dataset designed to support automatic fact‑checking in ten African languages and English, covering information retrieval, evidence extraction, and fact verification. Experiments show that current embedding models struggle with cross‑lingual retrieval, and that cultural and news documents are easier to retrieve than healthcare documents. Large language models also lack robust multilingual fact‑verification, but few‑shot prompting and task‑specific fine‑tuning can significantly improve performance.
By Israel Abebe Azime, Jesujoba Oluwadara Alabi, Crystina Zhang, Iffat Maab, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Folasade Peace Alabi, Salomey Osei, Saminu Mohammad Aliyu, Nkechinyere Faith Aguobi, Bontu Fufa Balcha, Blessing Kudzaishe Sibanda, Davis David, Mouhamadane Mboup, Daud Abolade, Neo Putini, Philipp Slusallek, David Ifeoluwa Adelani, Dietrich Klakow
arXiv:2606. 24460v1 Announce Type: cross Abstract: Commercial large language models bill, scale latency, and budget context per token.
By Olaoye Anthony Somide
The paper "Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities" introduces the Waldo benchmark, a multilingual QA dataset built from Wikipedia that focuses on knowledge gaps and conflicts across languages. It evaluates eight models in five languages and finds that when a fact is missing in one language, models tend to use evidence from the other language, but when conflicting accounts exist, responses align strongly with the query language, leading to different answers for semantically identical questions. The study also explores mitigation strategies, including ablating attention heads and LoRA-based training, which can reduce the preference gap by up to 61.5%.
By Dayeon Ki, Ruochen Zhang, Silviu Cucerzan, Ryen W. White, Ning Gao
The study evaluates six state‑of‑the‑art large language models on 400 adversarial prompts in Yoruba and Nigerian Pidgin, categorised into four harm types. Native‑speaker annotators scored responses and assessed reliability with Cohen’s κ, revealing that many models failed to understand the prompts, leading to low rejection rates in the African languages. Harmful content was frequently produced, with attack success rates higher in Nigerian languages and significant cross‑lingual safety gaps, especially for GPT‑4o Mini and Aya 101.
By Paula Chikaodinaka Anyanwu, Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Ifeoluwa Dorcas Awotunde, Danielle Blanche Kapsa, Tassallah Abdullahi, Clemencia Siro, Idris Abdulmumin, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad
The paper introduces CultureLens, a benchmark with 9,261 scenarios from 75 countries that tests how large language models (LLMs) incorporate cultural information. Experiments on Llama‑3.1‑8B, Gemma‑3‑12B, and Qwen‑3.5‑27B show that presenting a contradictory cultural norm reduces aligned answers by 32‑47 percentage points, even when the model knows the correct country. Fine‑tuning with contradictory‑norm examples improves alignment to 85‑93% under contradictory norms while only slightly affecting performance under true norms.
By Samee Arif, Furkan Danisman, Angana Borah, Honglak Lee, Dehan Kong, Lajanugen Logeswaran, Moontae Lee, Zhijing Jin, Rada Mihalcea
The paper presents Sometin Beta Pass Notin (SBPN), a multilingual ASR framework for Nigerian languages that uses a two‑stage knowledge‑distillation approach. First, student‑teacher distillation from monolingual models is conditioned on language‑specific N‑gram language models; second, iterative self‑improvement with pseudo‑labelled data further refines accuracy. The method reduces relative WER by 29% over monolingual baselines and outperforms state‑of‑the‑art multilingual models on Common Voice and FLEURS benchmarks for Yoruba, Hausa, Igbo, Nigerian Pidgin, and Nigerian English.
By Sewade Ogun