Papers by Abraham Toluwase Owodunni
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (2025.acl-long)
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Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu
| Challenge: | Recent advances in large language models (LLMs) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. |
| Approach: | They introduce AfriMed-QA, the first largescale Pan-African English multi-specialty medical Question-Answering (QA) dataset, with 15,000 questions sourced from over 60 medical schools across 16 countries. |
| Outcome: | The proposed model outperforms other models in the medical field and is compared with other models. |
UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages. (2026.findings-acl)
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Tassallah Abdullahi, Macton Mgonzo, Mardiyyah Oduwole, Paul Okewunmi, Abraham Toluwase Owodunni, Ritambhara Singh, Carsten Eickhoff
| Challenge: | Current guardian models are predominantly Western-centric and optimized for high-resource languages . low-resourced African languages are vulnerable to evolving harms, cross-lingual failures, cultural misalignment . |
| Approach: | They propose a policy-based safety benchmark for African languages built from adversarial queries authored by 155 domain experts across sensitive fields. |
| Outcome: | The proposed model overestimates multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models struggle to localize African-language contexts. |
A Decade of Scholarly Research on Open Knowledge Graphs (2024.lrec-main)
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Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, Mohamed Ben Aouicha
| Challenge: | Several literature surveys have been done to understand how open knowledge graphs are constructed, evaluated, and integrated. |
| Approach: | They analyze 4445 scholarly articles retrieved from Scopus and analyze their results to identify trends, patterns, and impact of research in this field. |
| Outcome: | The results reveal an ever-increasing number of publications on open knowledge graphs published every year, especially in developed countries (+50 per year). |
FLEXITOKENS: Flexible Tokenization for Evolving Language Models (2026.findings-acl)
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| Challenge: | Widely used subword tokenizers overfragment sequences in unseen domains, languages, and scripts . inefficient tokenizer models can cause overfragments in out-of-distribution domains if not trained properly . |
| Approach: | They propose a byte-level LM with learnable tokenizers to make tokenization adaptive . they propose 'flexitoken' which enables significantly greater flexibility during adaptation . |
| Outcome: | The proposed method significantly reduces token overfragmentation and improves on multilingual benchmarks and domains. |