Papers by Foutse Yuehgoh
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. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models (2025.naacl-long)
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David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, Jian Yun Zhuang, Jesujoba Oluwadara Alabi, Xuanli He, Millicent Ochieng, Sara Hooker, Andiswa Bukula, En-Shiun Annie Lee, Chiamaka Ijeoma Chukwuneke, Happy Buzaaba, Blessing Kudzaishe Sibanda, Godson Koffi Kalipe, Jonathan Mukiibi, Salomon Kabongo Kabenamualu, Foutse Yuehgoh, Mmasibidi Setaka, Lolwethu Ndolela, Nkiruka Odu, Rooweither Mabuya, Salomey Osei, Shamsuddeen Hassan Muhammad, Sokhar Samb, Tadesse Kebede Guge, Tombekai Vangoni Sherman, Pontus Stenetorp
| Challenge: | Large language models (LLMs) are limited to a few high-resource languages . many low-resourced languages are evaluated only on basic text classification tasks . |
| Approach: | They propose to use IrokoBench to evaluate 17 low-resource African languages . they use human-translated benchmark datasets to evaluate zero-shot, few-shot and translate-test settings . |
| Outcome: | The proposed model performs well in English and French, but the highest performing model perform poorly in proprietary models. |