David Ifeoluwa Adelani

Biographie

David Adelani est professeur adjoint en science informatique et lutte contre les inégalités à l’Université McGill, et membre académique principal à Mila – Institut québécois d'intelligence artificielle. Ses recherches se concentrent sur le traitement multilingue du langage naturel, avec un accent particulier sur les langues sous-dotées en ressources.

Étudiants actuels

Henok Ademtew

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Virginia Ceccatelli

Stagiaire de recherche - McGill

Blaise Cruz

Collaborateur·rice de recherche - McGill

Doctorat - McGill

Yejin Jeon

Postdoctorat - McGill

Gagan Jhajj

Stagiaire de recherche - McGill

Senyu Li Li

Doctorat - McGill

Yinhan Lu

Stagiaire de recherche - McGill

Doctorat - McGill

Damaris Stephanie NDJEBAYI

Doctorat - McGill

Juhyun Oh

Stagiaire de recherche - McGill

Jessica Ojo

Maîtrise recherche - McGill

Kosei Uemura

Stagiaire de recherche - McGill

Vivek Verma

Maîtrise professionnelle - UdeM

Tianyi Xu Xu

Stagiaire de recherche - McGill

Peter Yu

Maîtrise recherche - McGill

Publications

mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks

Luel Hagos Beyene

Vivek Verma

Min Ma

Jesujoba Oluwadara Alabi

Fabian David Schmidt

Joyce Nakatumba-Nabende

Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as spe… (voir plus)ech. However, their evaluation is often limited to English and a few high-resource languages. For low-resource languages, there is no standardized evaluation benchmark. In this paper, we address this gap by introducing mSTEB, a new benchmark to evaluate the performance of LLMs on a wide range of tasks covering language identification, text classification, question answering, and translation tasks on both speech and text modalities. We evaluated the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B. Our evaluation shows a wide gap in performance between high-resource and low-resource languages, especially for languages spoken in Africa and Americas/Oceania. Our findings show that more investment is needed to address their under-representation in LLMs coverage.

2025-06-09

ArXiv (prépublication)

SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?

Senyu Li

Jiayi Wang

Felermino Dario Mario Ali

Colin Cherry

Daniel Deutsch

Eleftheria Briakou

Rui Sousa-Silva

Henrique Lopes Cardoso

Pontus Stenetorp

Evaluating machine translation (MT) quality for under-resourced African languages remains a significant challenge, as existing metrics often… (voir plus) suffer from limited language coverage and poor performance in low-resource settings. While recent efforts, such as AfriCOMET, have addressed some of the issues, they are still constrained by small evaluation sets, a lack of publicly available training data tailored to African languages, and inconsistent performance in extremely low-resource scenarios. In this work, we introduce SSA-MTE, a large-scale human-annotated MT evaluation (MTE) dataset covering 13 African language pairs from the News domain, with over 63,000 sentence-level annotations from a diverse set of MT systems. Based on this data, we develop SSA-COMET and SSA-COMET-QE, improved reference-based and reference-free evaluation metrics. We also benchmark prompting-based approaches using state-of-the-art LLMs like GPT-4o and Claude. Our experimental results show that SSA-COMET models significantly outperform AfriCOMET and are competitive with the strongest LLM (Gemini 2.5 Pro) evaluated in our study, particularly on low-resource languages such as Twi, Luo, and Yoruba. All resources are released under open licenses to support future research.

2025-05-31

arXiv (publié)

Improving Multilingual Math Reasoning for African Languages

Odunayo Ogundepo

Akintunde Oladipo

Kelechi Ogueji

Esther Adenuga

Jimmy Lin

Researchers working on low-resource languages face persistent challenges due to limited data availability and restricted access to computati… (voir plus)onal resources. Although most large language models (LLMs) are predominantly trained in high-resource languages, adapting them to low-resource contexts, particularly African languages, requires specialized techniques. Several strategies have emerged for adapting models to low-resource languages in todays LLM landscape, defined by multi-stage pre-training and post-training paradigms. However, the most effective approaches remain uncertain. This work systematically investigates which adaptation strategies yield the best performance when extending existing LLMs to African languages. We conduct extensive experiments and ablation studies to evaluate different combinations of data types (translated versus synthetically generated), training stages (pre-training versus post-training), and other model adaptation configurations. Our experiments focuses on mathematical reasoning tasks, using the Llama 3.1 model family as our base model.

2025-04-30

arXiv (publié)

The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages

Chris Emezue

The NaijaVoices Community

Busayo Awobade

Abraham Owodunni

Handel Emezue

Gloria Monica Tobechukwu Emezue

N. N. Emezue

Sewade Ogun

Bunmi Akinremi

Christopher Pal

2025-04-30

arXiv (publié)

Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs

A. Seza Doğruöz

Iyanuoluwa Shode

Aremu Anuoluwapo

2025-03-31

Findings of the Association for Computational Linguistics: NAACL 2025 (publié)

Lugha-Llama: Adapting Large Language Models for African Languages

Happy Buzaaba

Alexander Wettig

Christiane Fellbaum

2025-03-31

arXiv (publié)

SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection

Shamsuddeen Hassan Muhammad

Nedjma OUSIDHOUM

Idris Abdulmumin

Seid Muhie Yimam

Jan Philip Wahle

Terry Lima Ruas

Meriem Beloucif

Christine de Kock

Tadesse Belay

Ibrahim Ahmad

Nirmal Surange

Daniela Teodorescu

Alham Fikri Aji

Felermino Ali

Vladimir Araujo

Abinew Ayele

Oana Ignat

Alexander Panchenko

Yi Zhou … (voir 1 de plus)

Saif M. Mohammad

2025-02-28

arXiv (publié)

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

Shamsuddeen Hassan Muhammad

Nedjma OUSIDHOUM

Idris Abdulmumin

Jan Philip Wahle

Terry Lima Ruas

Meriem Beloucif

Christine de Kock

Nirmal Surange

Daniela Teodorescu

Ibrahim Ahmad

Alham Fikri Aji

Felermino Ali

Ilseyar Alimova

Vladimir Araujo

Nikolay Babakov

Naomi Baes

Ana-Maria Bucur

Andiswa Bukula

Guanqun Cao … (voir 28 de plus)

Rodrigo Tufino Cardenas

Rendi Chevi

Chiamaka Ijeoma Chukwuneke

Alexandra Ciobotaru

Daryna Dementieva

Murja Sani Gadanya

Robert Geislinger

Bela Gipp

Oumaima Hourrane

Oana Ignat

Falalu Lawan

Rooweither Mabuya

Rahmad Mahendra

Vukosi Marivate

Andrew Piper

Alexander Panchenko

Charles Henrique Porto Ferreira

Vitaly Protasov

Samuel Rutunda

Manish Shrivastava

Aura Cristina Udrea

Lilian D. A. Wanzare

Sophie Wu

Florian Valentin Wunderlich

Hanif Muhammad Zhafran

Tianhui Zhang

Yi Zhou

Saif M. Mohammad

2025-02-16

ArXiv (prépublication)

INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages

Hao Yu

Jesujoba Oluwadara Alabi

Andiswa Bukula

Zhuang Yun Jian

En-Shiun Annie Lee

Tadesse Kebede Guge

Israel Abebe Azime

Happy Buzaaba

Blessing Kudzaishe Sibanda

Godson Kalipe

Jonathan Mukiibi

S. Kabenamualu

M. Setaka

Lolwethu Ndolela

Nkiruka Bridget Odu

Rooweither Mabuya

Shamsuddeen Hassan Muhammad

Salomey Osei

Sokhar Samb

Juliet W. Murage … (voir 2 de plus)

Dietrich Klakow

2025-02-12

ArXiv (prépublication)

AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages

Shamsuddeen Hassan Muhammad

Idris Abdulmumin

Abinew Ayele

Ibrahim Ahmad

Saminu Mohammad Aliyu

Nelson Odhiambo Onyango

Lilian D. A. Wanzare

Samuel Rutunda

Lukman Jibril Aliyu

Esubalew Alemneh

Oumaima Hourrane

Hagos Gebremichael

Elyas Abdi Ismail

Meriem Beloucif

Ebrahim Chekol Jibril

Andiswa Bukula

Rooweither Mabuya

Salomey Osei

Abigail Oppong … (voir 7 de plus)

Tadesse Belay

Tadesse Kebede Guge

Tesfa Tegegne Asfaw

Chiamaka Ijeoma Chukwuneke

Paul Rottger

Seid Muhie Yimam

Nedjma OUSIDHOUM

Hate speech and abusive language are global phenomena that need socio-cultural background knowledge to be understood, identified, and modera… (voir plus)ted. However, in many regions of the Global South, there have been several documented occurrences of (1) absence of moderation and (2) censorship due to the reliance on keyword spotting out of context. Further, high-profile individuals have frequently been at the center of the moderation process, while large and targeted hate speech campaigns against minorities have been overlooked. These limitations are mainly due to the lack of high-quality data in the local languages and the failure to include local communities in the collection, annotation, and moderation processes. To address this issue, we present AfriHate: a multilingual collection of hate speech and abusive language datasets in 15 African languages. Each instance in AfriHate is annotated by native speakers familiar with the local culture. We report the challenges related to the construction of the datasets and present various classification baseline results with and without using LLMs. The datasets, individual annotations, and hate speech and offensive language lexicons are available on https://github.com/AfriHate/AfriHate

2025-01-13

ArXiv (prépublication)

AFRIDOC-MT: Document-level MT Corpus for African Languages

Jesujoba Oluwadara Alabi

Israel Abebe Azime

Miaoran Zhang

Cristina España-Bonet

Rachel Bawden

Dawei Zhu

Clement Odoje

Idris Akinade

Iffat Maab

Davis David

Shamsuddeen Hassan Muhammad

Neo Putini

David O. Ademuyiwa

Andrew Caines

Dietrich Klakow

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, … (voir plus)Hausa, Swahili, Yor\`ub\'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.

2024-12-31

EMNLP (publié)

Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead

Jesujoba Oluwadara Alabi

Michael A. Hedderich

Dietrich Klakow

2024-12-31

EMNLP (publié)