David Ifeoluwa Adelani

Biography

David Adelani is an assistant professor at McGill University’s School of Computer Science under the Fighting Inequities initiative, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

Adelani’s research focuses on multilingual natural language processing with special attention to under-resourced languages.

Current Students

Henok Ademtew

Master's Research - McGill University

Master's Research - McGill University

Virginia Ceccatelli

Collaborating researcher - McGill University

David Guzmán Ramírez

PhD - McGill University

Research Intern - McGill University

Research Intern - McGill University

Yejin Jeon

Postdoctorate - McGill University

Senyu Li Li

PhD - McGill University

Yinhan Lu

Collaborating researcher - McGill University

Marie Maltais

PhD - McGill University

Damaris Stephanie NDJEBAYI

PhD - McGill University

Juhyun Oh

Collaborating Alumni - McGill University

Jessica Ojo

Master's Research - McGill University

Kosei Uemura

Research Intern - McGill University

Vivek Verma

Professional Master's - Université de Montréal

Jessica Wang

Research Intern - McGill University

Tianyou Xie

Research Intern - McGill University

Tianyi Xu Xu

Research Intern - McGill University

Peter Yu

Collaborating Alumni - McGill University

Publications

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 … (see 28 more)

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 (preprint)

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 … (see 2 more)

Dietrich Klakow

2025-02-12

ArXiv (preprint)

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 … (see 7 more)

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… (see more)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 (preprint)

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, … (see more)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 (published)

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 (published)

Multilingual Language Model Pretraining using Machine-translated Data

Jiayi Wang

Yao Lu

Maurice Weber

Max Ryabinin

Yihong Chen

Raphael Tang

Pontus Stenetorp

2024-12-31

EMNLP (published)

Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?

Jonah Dauvet

Min Ma

Jessica Ojo

2024-12-31

Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) (published)

Training of LLM-Based List-Wise Multilingual Reranker

Hao Yu

2024-12-31

Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) (published)

Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generation

Senyu Li

Zipeng Sun

Jiayi Wang

Xue Liu

Pontus Stenetorp

Siva Reddy

Traditional supervised fine-tuning (SFT) strategies for sequence-to-sequence tasks often train models to directly generate the target output… (see more). Recent work has shown that guiding models with intermediate steps, such as keywords, outlines, or reasoning chains, can significantly improve performance, coherence, and interpretability. However, these methods often depend on predefined intermediate formats and annotated data, limiting their scalability and generalizability. In this work, we introduce a task-agnostic framework that enables models to generate intermediate "warmup" sequences. These warmup sequences, serving as an initial state for subsequent generation, are optimized to enhance the probability of generating the target sequence without relying on external supervision or human-designed structures. Drawing inspiration from reinforcement learning principles, our method iteratively refines these intermediate steps to maximize their contribution to the final output, similar to reward-driven optimization in reinforcement learning with human feedback. Experimental results across tasks such as translation, summarization, and multi-choice question answering for logical reasoning show that our approach outperforms traditional SFT methods, and offers a scalable and flexible solution for sequence-to-sequence tasks.

2024-12-31

Association for Computational Linguistics (published)

The Responsible Foundation Model Development Cheatsheet: A Review of Tools&Resources

Shayne Longpre

Stella Biderman

Alon Albalak

Hailey Schoelkopf

Daniel McDuff

Sayash Kapoor

Kevin Klyman

Kyle Lo

Gabriel Ilharco

Nay San

Maribeth Rauh

Aviya Skowron

Bertie Vidgen

Laura Weidinger

Arvind Narayanan

Victor Sanh

Percy Liang

Rishi Bommasani

Peter Henderson … (see 3 more)

Sasha Luccioni

Yacine Jernite

Luca Soldaini

2024-12-06

TMLR (accepted)

openreview.net

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

Shivalika Singh

Angelika Romanou

Cl'ementine Fourrier

Jian Gang Ngui

Daniel Vila-Suero

Peerat Limkonchotiwat

Kelly Marchisio

Wei Qi Leong

Yosephine Susanto

Raymond Ng

Shayne Longpre

Wei-Yin Ko

Madeline Smith

Antoine Bosselut

Alice Oh

André F. T. Martins

Leshem Choshen

Daphne Ippolito

Enzo Ferrante … (see 3 more)

Marzieh Fadaee

Beyza Ermis

Sara Hooker

2024-12-03

ArXiv (preprint)

Uhura: A Benchmark for Evaluating Scientific Question Answering and Truthfulness in Low-Resource African Languages

Edward Bayes

Israel Abebe Azime

Jesujoba Oluwadara Alabi

Jonas Kgomo

Tyna Eloundou

Elizabeth Proehl

Kai Chen

Imaan Khadir

Naome Etori

Shamsuddeen Hassan Muhammad

C. Mpanza

Igneciah Pocia Thete

Dietrich Klakow

Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primari… (see more)ly because datasets for low-resource languages (LRLs) are scarce. In this paper, we present Uhura -- a new benchmark that focuses on two tasks in six typologically-diverse African languages, created via human translation of existing English benchmarks. The first dataset, Uhura-ARC-Easy, is composed of multiple-choice science questions. The second, Uhura-TruthfulQA, is a safety benchmark testing the truthfulness of models on topics including health, law, finance, and politics. We highlight the challenges creating benchmarks with highly technical content for LRLs and outline mitigation strategies. Our evaluation reveals a significant performance gap between proprietary models such as GPT-4o and o1-preview, and Claude models, and open-source models like Meta's LLaMA and Google's Gemma. Additionally, all models perform better in English than in African languages. These results indicate that LMs struggle with answering scientific questions and are more prone to generating false claims in low-resource African languages. Our findings underscore the necessity for continuous improvement of multilingual LM capabilities in LRL settings to ensure safe and reliable use in real-world contexts. We open-source the Uhura Benchmark and Uhura Platform to foster further research and development in NLP for LRLs.

2024-11-30

ArXiv (preprint)