Papers by Israel Abebe Azime
Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches (2025.findings-emnlp)
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Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja, Mario Fritz, Isabel Valera, Dietrich Klakow, Philipp Slusallek
| Challenge: | Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks such as loan approvals. |
| Approach: | They evaluate the performance and fairness of LLMs on serialized loan approval datasets from Ghana, Germany, and the United States. |
| Outcome: | The model’s zero-shot and in-context learning (ICL) capabilities are evaluated on loan approval datasets from Ghana, Germany, and the United States. |
MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
| Approach: | They propose to create a dataset for named entity recognition (NER) in ten African languages. |
| Outcome: | The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP. |
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets (2022.tacl-1)
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Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
| Challenge: | Lower-resource corpora have systematic issues, including mislabeled or nonstandard/ambiguous language codes. |
| Approach: | They manually audit the quality of 205 language-specific corpora released with five major public datasets. |
| Outcome: | The results show that lower-resource corpora have systematic issues even for non-proficient speakers. |
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text (2025.findings-emnlp)
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Tadesse Destaw Belay, Israel Abebe Azime, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Idris Abdulmumin, Abinew Ali Ayele, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | Domain adaptive pre-training and task-adaptive pre- training (TAPT) are popular methods to reduce this bias for low-resource languages, but they have not been explored for African multilingual encoders. |
| Approach: | They propose a large-scale social media and news domain corpus for continual pre-training on African languages. |
| Outcome: | The proposed methods improve performance on three subjective tasks, including sentiment analysis, multi-label emotion, and hate speech classification, while TAPT improves performance on other related tasks. |
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |
Afri-MCQA: Multimodal Cultural Question Answering for African Languages (2026.acl-long)
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Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva, Israel Abebe Azime, Jesujoba Oluwadara Alabi, Muhidin A. Mohamed, Debela Desalegn Yadeta, Negasi Haile Abadi, Abigail Oppong, Nnaemeka Casmir Obiefuna, Idris Abdulmumin, Naome A Etori, Eric Peter Wairagala, Kanda Patrick Tshinu, Imanigirimbabazi Emmanuel, Gabofetswe Malema, Alham Fikri Aji, David Ifeoluwa Adelani, Thamar Solorio
| Challenge: | Afri-MCQA is the first multilingual cultural question-answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. |
| Approach: | They introduce Afri-MCQA, the first multilingual cultural question-answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. |
| Outcome: | The proposed model shows poor performance across cultures, with near zero accuracy on open-ended VQA when queried through native language or speech. |
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation (2024.lrec-main)
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Atnafu Lambebo Tonja, Israel Abebe Azime, Tadesse Destaw Belay, Mesay Gemeda Yigezu, Moges Ahmed Ah Mehamed, Abinew Ali Ayele, Ebrahim Chekol Jibril, Michael Melese Woldeyohannis, Olga Kolesnikova, Philipp Slusallek, Dietrich Klakow, Seid Muhie Yimam
| Challenge: | Low-resource languages are lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. |
| Approach: | They propose to use multilingual large language models for five Ethiopian languages and a benchmark dataset to evaluate their performance. |
| Outcome: | The proposed models outperform existing models in five Ethiopian languages and a benchmark dataset for various downstream NLP tasks. |
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation (2025.findings-emnlp)
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Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, Thamar Solorio
| Challenge: | a human-curated benchmark of over 5,800 triples of images is used to evaluate multimodal translation systems. |
| Approach: | They introduce a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. |
| Outcome: | The results show that visual context improves translation quality in culturally-specific items . |
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding (2025.coling-main)
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Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov, Dietrich Klakow, Philip Slusallek, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Emotion classification is one of the most challenging tasks in large language models. |
| Approach: | They propose to use a multi-label emotion classification dataset for four Ethiopian languages to evaluate their ability to learn and reason. |
| Outcome: | The proposed model improves the understanding of emotions in language models and how people convey emotions through various languages. |
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. |
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding (2025.findings-naacl)
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Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Yonas Chanie, Bontu Fufa Balcha, Negasi Haile Abadi, Henok Biadglign Ademtew, Mulubrhan Abebe Nerea, Debela Desalegn Yadeta, Derartu Dagne Geremew, Assefa Atsbiha Tesfu, Philipp Slusallek, Thamar Solorio, Dietrich Klakow
| Challenge: | Large language models (LLMs) evaluation is gaining increasing attention as they are typically trained on general-domain datasets while demonstrating notable performance on tasks out of their training domains. |
| Approach: | They propose an LLM evaluation benchmark for low-resource languages that focuses on low-rsource language understanding in culture-specific scenarios. |
| Outcome: | The proposed benchmarks outperform monolingual evaluations on proverb generation tasks and native language proverb descriptions on multiple choice tasks. |
INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages (2025.acl-long)
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Hao Yu, Jesujoba Oluwadara Alabi, Andiswa Bukula, Jian Yun Zhuang, En-Shiun Annie Lee, Tadesse Kebede Guge, Israel Abebe Azime, Happy Buzaaba, Blessing Kudzaishe Sibanda, Godson Koffi Kalipe, Jonathan Mukiibi, Salomon Kabongo Kabenamualu, Mmasibidi Setaka, Lolwethu Ndolela, Nkiruka Odu, Rooweither Mabuya, Shamsuddeen Hassan Muhammad, Salomey Osei, Sokhar Samb, Dietrich Klakow, David Ifeoluwa Adelani
| Challenge: | Slot-filling and intent detection tasks are well-established tasks in Conversational AI, but current benchmarks for these tasks rely on evaluations of low-resource languages and translations from English benchmarks. |
| Approach: | They propose to use a multilingual, open-source benchmark dataset for 16 African languages with utterances generated by native speakers across diverse domains. |
| Outcome: | The proposed dataset compares multilingual transformer models and prompting large language models (LLMs) with the English language. |
Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages (2026.findings-acl)
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| Challenge: | Existing multilingual benchmarks that use translations retain English-centric entities. |
| Approach: | They propose a framework that culturally localizes translated datasets into variants enriched with local entities. |
| Outcome: | The proposed framework mitigates English-centric entity bias and improves model robustness when native entities are introduced across languages. |