David Ifeoluwa Adelani

2025-02-13

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

Slot-filling and intent detection are well-established tasks in Conversational AI. However, current large-scale benchmarks for these tasks o… (see more)ften exclude evaluations of low-resource languages and rely on translations from English benchmarks, thereby predominantly reflecting Western-centric concepts. In this paper, we introduce Injongo -- a multicultural, open-source benchmark dataset for 16 African languages with utterances generated by native speakers across diverse domains, including banking, travel, home, and dining. Through extensive experiments, we benchmark the fine-tuning multilingual transformer models and the prompting large language models (LLMs), and show the advantage of leveraging African-cultural utterances over Western-centric utterances for improving cross-lingual transfer from the English language. Experimental results reveal that current LLMs struggle with the slot-filling task, with GPT-4o achieving an average performance of 26 F1-score. In contrast, intent detection performance is notably better, with an average accuracy of 70.6%, though it still falls behind the fine-tuning baselines. Compared to the English language, GPT-4o and fine-tuning baselines perform similarly on intent detection, achieving an accuracy of approximately 81%. Our findings suggest that the performance of LLMs is still behind for many low-resource African languages, and more work is needed to further improve their downstream performance.

2025-02-13

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

2025-01-14

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

2025-01-14

ArXiv (preprint)

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

Jesujoba Oluwadara Alabi

Israel Abebe Azime

Miaoran Zhang

Cristina España-Bonet

Rachel Bawden

D. 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.

2025-01-10

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.

2025-01-10

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.

2025-01-10

ArXiv (preprint)

AfriHG: News headline generation for African Languages

Toyib Ogunremi

Serah Akojenu

Anthony Soronnadi

Olubayo Adekanmbi

This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 l… (see more)anguages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multilingual mT5-base model. Finally, we show that the performance of fine-tuning AfriTeVa V2 with 313M parameters is competitive to prompting Aya-101 LLM with more than 13B parameters.

2024-12-28

ArXiv (preprint)

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

2024-12-07

TMLR (accepted)

openreview.net

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

Choice 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.

2024-12-01

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

Choice 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.

2024-12-01

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

Choice 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.

2024-12-01

ArXiv (preprint)