Papers by Ifeoma Okoh
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs (2026.acl-long)
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Zohaib Khan, Mustafa Dogan, Ifeoma Okoh, Pouya Sadeghi, Siddhartha Shrestha, Sergius Justus Chesami Nyah, Mahmoud O. Mokhiamar, Michael J Ryan, Tarek Naous
| Challenge: | Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. |
| Approach: | They introduce a multilingual parallel dataset of 440 misinformation generation prompt templates and 6,867 entities, spanning 8 languages and 195 countries. |
| Outcome: | The proposed model reduces misinformation generation across languages and countries . it also reduces the risk of misinformation being spread across countries based on the model's performance . |
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. |
The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment (2024.lrec-main)
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Chris Chinenye Emezue, Ifeoma Okoh, Chinedu Emmanuel Mbonu, Chiamaka Chukwuneke, Daisy Monika Lal, Ignatius Ezeani, Paul Rayson, Ijemma Onwuzulike, Chukwuma Onyebuchi Okeke, Gerald Okey Nweya, Bright Ikechukwu Ogbonna, Chukwuebuka Uchenna Oraegbunam, Esther Chidinma Awo-Ndubuisi, Akudo Amarachukwu Osuagwu
| Challenge: | UNESCO projects that the Igbo language will be endangered by 2025 . primary obstacle in developing dialectal-aware language technologies is lack of comprehensive dialectal datasets. |
| Approach: | They propose to use a multi-dialectal Igbo-English dictionary dataset to enhance the representation of Igbe dialects. |
| Outcome: | The proposed dataset enables machine translation systems to handle dialect variations in sentences. |
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning (2024.acl-long)
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Shivalika Singh, Freddie Vargus, Daniel D’souza, Börje Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura O’Mahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergun, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Chien, Sebastian Ruder, Surya Guthikonda, Emad Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, Sara Hooker
| Challenge: | Existing datasets in the English language are mostly in the realm of instruction fine-tuning . aya dataset, the Aya Collection, and the AYa Evaluation Suite are key resources . |
| Approach: | They aim to build a human-curated instruction-following dataset spanning 65 languages . they work with fluent speakers of languages from around the world to collect natural instances of instructions and completions . |
| Outcome: | The goal is to build a human-curated instruction-following dataset spanning 65 languages. |