GenAI4ELab/papercli-papers huggingface dataset:
Exploring Transliteration-Based Zero-Shot Transfer for Amharic ASR
Authors: Hellina Hailu Nigatu; Hanan Aldarmaki
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: The performance of Automatic Speech Recognition (ASR) depends on the availability of transcribed speech datasets—often scarce ornon-existent for many of the worlds languages. This study investigates alternative strategies to bridge the data gap using zero-shot cross-lingual transfer, leveraging transliteration as a method to utilize data from other languages. We experiment with transliteration from various source languages and demonstrate ASR performance in a low-resourced language, Amharic. We find that source data that align with the character distribution of the test data achieves the best performance, regardless of language family. We also experiment with fine-tuning with minimal transcribed data in the target language. Our findings demonstrate that transliteration, particularly when combined with a strategic choice of source languages, is a viable approach for improving ASR in zero-shot and low-resourced settings.
Fine-tuning Whisper Tiny for Swahili ASR: Challenges and Recommendations for Low-Resource Speech Recognition
Authors: Avinash Kumar Sharma; Manas Pandya; Arpit Shukla
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Automatic Speech Recognition (ASR) technologies have seen significant advancements, yet many widely spoken languages remain underrepresented. This paper explores the fine-tuning of OpenAI’s Whisper Tiny model (39M parameters) for Swahili, a lingua franca for over 100 million people across East Africa. Using a dataset of 5,520 Swahili audio samples, we analyze the model’s performance, error patterns, and limitations after fine-tuning. Our results demonstrate the potential of fine-tuning for improving transcription accuracy, while also highlighting persistent challenges such as phonetic misinterpretations, named entity recognition failures, and difficulties with morphologically complex words. We provide recommendations for improving Swahili ASR, including scaling to larger model variants, architectural adaptations for agglutinative languages, and data enhancement strategies. This work contributes to the growing body of research on adapting pre-trained multilingual ASR systems to low-resource languages, emphasizing the need for approaches that account for the unique linguistic features of Bantu languages.
Who Wrote This? Identifying Machine vs Human-Generated Text in Hausa
Authors: Babangida Sani; Aakansha Soy; Sukairaj Hafiz Imam; Ahmad Mustapha; Lukman Jibril Aliyu; Idris Abdulmumin; Ibrahim Said Ahmad; Shamsuddeen Hassan Muhammad
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: The advancement of large language models (LLMs) has allowed them to be proficient in various tasks, including content generation. However, their unregulated usage can lead to malicious activities such as plagiarism and generating and spreading fake news, especially for low-resource languages. Most existing machine-generated text detectors are trained on high-resource languages like English, French, etc. In this study, we developed the first large-scale detector that can distinguish between human- and machine-generated content in Hausa. We scraped seven Hausa-language media outlets for the human-generated text and the Gemini-2.0 flash model to automatically generate the corresponding Hausa-language articles based on the human-generated article headlines. We fine-tuned four pre-trained African-centric models (AfriTeVa, AfriBERTa, AfroX LMR, and AfroXLMR-76L) on the resulting dataset and assessed their performance using accuracy and F1-score metrics. AfroXLMR achieved the highest performance with an accuracy of 99.23% and an F1 score of 99.21%, demonstrating its effectiveness for Hausa text detection. Our dataset is made publicly available to enable further research.
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions
Authors: Sukairaj Hafiz Imam; Babangida Sani; Dawit Ketema Gete; Bedru Yimam Ahmed; Ibrahim Said Ahmad; Idris Abdulmumin; Seid Muhie Yimam; Muhammad Yahuza Bello; Shamsuddeen Hassan Muhammad
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Automatic Speech Recognition (ASR) technologies have transformed human-computer interaction; however, low-resource languages in Africa remain significantly underrepresented in both research and practical applications. This study investigates the major challenges hindering the development of ASR systems for these languages, which include data scarcity, linguistic complexity, limited computational resources, acoustic variability, and ethical concerns surrounding bias and privacy. The primary goal is to critically analyze these barriers and identify practical, inclusive strategies to advance ASR technologies within the African context. Recent advances and case studies emphasize promising strategies such as community-driven data collection, self-supervised and multilingual learning, lightweight model architectures, and techniques that prioritize privacy. Evidence from pilot projects involving various African languages showcases the feasibility and impact of customized solutions, which encompass morpheme-based modeling and domain-specific ASR applications in sectors like healthcare and education. The findings highlight the importance of interdisciplinary collaboration and sustained investment to tackle the distinct linguistic and infrastructural challenges faced by the continent. This study offers a progressive roadmap for creating ethical, efficient, and inclusive ASR systems that not only safeguard linguistic diversity but also improve digital accessibility and promote socioeconomic participation for speakers of African languages.
SabiYarn: Advancing Low Resource Languages with Multitask NLP Pretraining
Authors: Jeffrey Otoibhi; Oduguwa Damilola; Okpare David
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: The rapid advancement of large language models (LLMs) has revolutionized natural language processing, yet a significant challenge persists: the under representation of low-resource languages. This paper introduces SabiYarn, a novel 125M parameter decoder-only language model specifically designed to address this gap for Nigerian languages.Our research demonstrates that a relatively small language model can achieve remarkable performance across multiple languages even in a low-resource setting when trained on carefully curated task-specific datasets. We introduce a multitask learning framework designed for computational efficiency, leveraging techniques such as sequence packing to maximize token throughput per batch. This allows SabiYarn to make the most of a limited compute budget while achieving strong performance across multiple NLP tasks.This paper not only highlights the effectiveness of our approach but also challenges the notion that only massive models can achieve high performance in diverse linguistic contexts, outperforming models over 100 times its parameter size on specific tasks such as translation (in both directions), Named Entity Recognition, Text Diacritization, and Sentiment Analysis in the low-resource languages it was trained on. SabiYarn-125M represents a significant step towards democratizing NLP technologies for low-resource languages, offering a blueprint for developing efficient, high-performing models tailored to specific linguistic regions. Our work paves the way for more inclusive and culturally sensitive AI systems, potentially transforming how language technologies are developed and deployed in linguistically diverse areas like Nigeria and beyond.
Retrieval-Augmented Generation Meets Local Languages for Improved Drug Information Access and Comprehension.
Authors: Ahmad Ibrahim Ismail; Bashirudeen Opeyemi Ibrahim; Olubayo Adekanmbi; Ife Adebara
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Medication errors are among the leading causes of avoidable harm in healthcare systems across the world. A large portion of these errors stem from inefficient information retrieval processes and lack of comprehension of drug information. In low-resource settings, these issues are exacerbated by limited access to updated and reliable sources, technological constraints, and linguistic barriers. Innovations to improve the retrieval and comprehension of drug-related information are therefore poised to reduce medication errors and improve patient outcomes. This research employed open-source Retrieval-Augmented Generation (RAG) integrated with multilingual translation and Text-to-Speech (TTS) systems. Using open-source tools, a corpus was created from prominent sources of medical information in Nigeria and stored as high-level text embeddings in a Chroma database. Upon user query, relevant drug information is retrieved and synthesized using a large language model. This can be translated into Yoruba, Igbo, and Hausa languages, and converted into speech through the TTS system, addressing the linguistic accessibility gap. Evaluation of the system by domain experts indicated impressive overall performance in translation, achieving an average accuracy of 73%, and the best performance observed in Hausa and Yoruba. TTS results were moderately effective (mean = 57%), with Igbo scoring highest in speech clarity (68%). However, tonal complexity, especially in Yoruba, posed challenges for accurate pronunciation, highlighting the need for language-specific model fine-tuning. Addressing these linguistic nuances is essential to optimize comprehension and practical utility in diverse healthcare settings. The results demonstrates systems the potential to improve access to drug information, enhance comprehension, and reduce linguistic barriers. These technologies could substantially mitigate medication errors and improve patient safety. This study offers valuable insights and practical guidelines for future implementations aimed at strengthening global medication safety practices.
Story Generation with Large Language Models for African Languages
Authors: Catherine Nana Nyaah Essuman; Jan Buys
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: The development of Large Language Models (LLMs) for African languages has been hindered by the lack of large-scale textual data. Previous research has shown that relatively small language models, when trained on synthetic data generated by larger models, can produce fluent, short English stories, providing a data-efficient alternative to large-scale pretraining. In this paper, we apply a similar approach to develop and evaluate small language models for generating childrens stories in isiZulu and Yoruba, using synthetic datasets created through translation and multilingual prompting. We train six language-specific models varying in dataset size and source, and based on the GPT-2 architecture. Our results show that models trained on synthetic low-resource data are capable of producing coherent and fluent short stories in isiZulu and Yoruba. Models trained on larger synthetic datasets generally perform better in terms of coherence and grammar, and also tend to generalize better, as seen by their lower evaluation perplexities. Models trained on datasets generated through prompting instead of translation generate similar or more coherent stories and display more creativity, but perform worse in terms of generalization to unseen data. In addition to the potential educational applications of the automated story generation, our approach has the potential to be used as the foundation for more data-efficient low-resource language models.
Command R7B Arabic: a small, enterprise-focused, multilingual, and culturally aware Arabic LLM
Authors: Yazeed Alnumay; Alexandre Barbet; Anna Bialas; William Darling; Shaan Desai; Joan Devassy; Kyle Duffy; Stephanie Howe; Olivia Lasche; Justin Lee; Anirudh Shrinivason; Jennifer Tracey
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Building high-quality large language models (LLMs) for enterprise Arabic applications remains challenging due to the limited availability of digitized Arabic data. In this work, we present a data synthesis and refinement strategy to help address this problem, namely, by leveraging synthetic data generation and human-in-the-loop annotation to expand our Arabic training corpus. We further present our iterative post training recipe that is essential to achieving state-of-the-art performance in aligning the model with human preferences, a critical aspect to enterprise use cases. The culmination of this effort is the release of a small, 7B, open-weight model that outperforms similarly sized peers in head-to-head comparisons and on Arabic-focused benchmarks covering cultural knowledge, instruction following, RAG, and contextual faithfulness.
Challenges and Limitations in Gathering Resources for Low-Resource Languages: The Case of Medumba
Authors: Tatiana Moteu Ngoli; Mbuh Christabel; Njeunga Yopa
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Low-resource languages face significant challenges in natural language processing due to the scarcity of annotated data, linguistic resources, and the lack of language standardization, which leads to variations in grammar, vocabulary, and writing systems. This issue is particularly observed in many African languages, which significantly reduces their usability. To bridge this barrier, this paper investigates the challenges and limitations of collecting datasets for the Medumba language, a Grassfields Bantu language spoken in Cameroon, in the context of extremely low-resource natural language processing. We mainly focus on the specificity of this language, including its grammatical and lexical structure. Our findings highlight key barriers, including (1) the challenges in typing and encoding Latin scripts, (2) the absence of standardized translations for technical and scientific terms, and (3) the challenge of limited digital resources and financial constraints, highlighting the need to improve data strategies and collaboration to advance computational research on African languages. We hope that our study informs the development of better tools and policies to make knowledge platforms more accessible to extremely low-resource language speakers. We further discuss the representation of the language, data collection, parallel corpus development.
YodiV3: NLP for Togolese Languages with Eyaa-Tom Dataset and the Lom Metric
Authors: Bakoubolo Essowe Justin; Kodjo François Xegbe; Catherine Nana Nyaah Essuman; Afola Kossi Mawouéna Samuel
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Most of the 40+ languages spoken in Togo are severely under-represented in Natural Language Processing (NLP) resources. We present YodiV3, a comprehensive approach to developing NLP for ten Togolese languages (plus two major lingua francas) covering machine translation, speech recognition, text-to-speech, and language identification. We introduce Eyaa-Tom, a new multi-domain parallel corpus (religious, healthcare, financial, etc.) for these languages. We also propose the Lom metric, a scoring framework to quantify the AI-readiness of each language in terms of available resources. Our experiments demonstrate that leveraging large pretrained models (e.g.NLLB for translation, MMS for speech) with YodiV3 leads to significant improvements in low-resource translation and speech tasks. This work highlights the impact of integrating diverse data sources and pretrained models to bootstrap NLP for under-served languages, and outlines future steps for expanding coverage and capability.
Challenging Multimodal LLMs with African Standardized Exams: A Document VQA Evaluation
Authors: Victor Tolulope Olufemi; Oreoluwa Boluwatife Babatunde; Emmanuel Bolarinwa; Kausar Yetunde Moshood
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Despite rapid advancements in multimodal large language models (MLLMs), their ability to process low-resource African languages in document-based visual question answering (VQA) tasks remains limited. This paper evaluates three state-of-the-art MLLMs—GPT-4o, Claude-3.5 Haiku, and Gemini-1.5 Pro—on WAEC/NECO standardized exam questions in Yoruba, Igbo, and Hausa. We curate a dataset of multiple-choice questions from exam images and compare model accuracies across two prompting strategies: (1) using English prompts for African language questions, and (2) using native-language prompts. While GPT-4o achieves over 90% accuracy for English, performance drops below 40% for African languages, highlighting severe data imbalance in model training. Notably, native-language prompting improves accuracy for most models, yet no system approaches human-level performance, which reaches over 50% in Yoruba, Igbo, and Hausa. These findings emphasize the need for diverse training data, fine-tuning, and dedicated benchmarks that address the linguistic intricacies of African languages in multimodal tasks, paving the way for more equitable and effective AI systems in education.
MOZ-Smishing: A Benchmark Dataset for Detecting Mobile Money Frauds
Authors: Felermino D. M. A. Ali; Saide M. Saide; Rui Sousa-Silva; Henrique Lopes Cardoso
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Despite the increasing prevalence of smishing attacks targeting Mobile Money Transfer systems, there is a notable lack of publicly available SMS phishing datasets in this domain. This study seeks to address this gap by creating a specialized dataset designed to detect smishing attacks aimed at Mobile Money Transfer users. The data set consists of crowd-sourced text messages from Mozambican mobile users, meticulously annotated into two categories: legitimate messages (ham) and fraudulent smishing attempts (spam). The messages are written in Portuguese, often incorporating microtext styles and linguistic nuances unique to the Mozambican context.We also investigate the effectiveness of LLMs in detecting smishing. Using in-context learning approaches, we evaluate the models’ ability to identify smishing attempts without requiring extensive task-specific training. The data set is released under an open license at the following link: huggingface-Anonymous
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
Authors: Pratik Rakesh Singh; Kritarth Prasad; Mohammadi Zaki; Pankaj Wasnik
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Neural Machine Translation (NMT) systems face significant challenges when working with low-resource languages, particularly in domain adaptation tasks. These difficulties arise due to limited training data and suboptimal model generalization, As a result, selecting an optimal model for translation is crucial for achieving strong performance on in-domain data, particularly in scenarios where fine-tuning is not feasible or practical. In this paper, we investigate strategies for selecting the most suitable NMT model for a given domain using bandit-based algorithms, including Upper Confidence Bound, Linear UCB, Neural Linear Bandit, and Thompson Sampling. Our method effectively addresses the resource constraints by facilitating optimal model selection with high confidence. We evaluate the approach across three African languages and domains, demonstrating its robustness and effectiveness in both scenarios where target data is available and where it is absent.
HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing
Authors: Shamsuddeen Hassan Muhammad; Ibrahim Said Ahmad; Idris Abdulmumin; Falalu Ibrahim Lawan; Sukairaj Hafiz Imam; Yusuf Aliyu; Sani Abdullahi Sani; Ali Usman Umar; Tajuddeen Gwadabe; Kenneth Church; Vukosi Marivate
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Hausa Natural Language Processing (NLP) has gained increasing attention in recent years, yet remains understudied as a low-resource language despite having over 120 million first-language (L1) and 80 million second-language (L2) speakers worldwide. While significant advances have been made in high-resource languages, Hausa NLP faces persistent challenges including limited open-source datasets and inadequate model representation. This paper presents an overview of the current state of Hausa NLP, systematically examining existing resources, research contributions, and gaps across fundamental NLP tasks: text classification, machine translation, named entity recognition, speech recognition, and question answering. We introduce HausaNLP, a curated catalog that aggregates datasets, tools, and research works to enhance accessibility and drive further development. Furthermore, we discuss challenges in integrating Hausa into large language models (LLMs), addressing issues of suboptimal tokenization, and dialectal variation. Finally, we propose strategic research directions emphasizing dataset expansion, improved language modeling approaches, and strengthened community collaboration to advance Hausa NLP. Our work provides both a foundation for accelerating Hausa NLP progress and valuable insights for broader multilingual NLP research.
Beyond Generalization :Evaluating Multilingual LLMs for Yorùbá Animal Health Translation
Authors: Godwin Adegbehingbe; Anthony Soronnadi; Ife Adebara; Olubayo Adekanmbi
Venue: ACL 2025 (Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025))

Abstract: Machine translation (MT) has advanced significantly for high-resource languages, yet specialized domain translation remains a challenge for low-resource languages. This study evaluates the ability of state-of-the-art multilingual models to translate animal health reports from English to Yorùbá, a crucial task for veterinary communication in underserved regions. We curated a dataset of 1,468 parallel sentences and compared multiple MT models in zero-shot and fine-tuned settings. Our findings indicate substantial limitations in their ability to generalize to domain-specific translation, with common errors arising from vocabulary mismatch, training data scarcity, and morphological complexity. Fine-tuning improves performance, particularly for the NLLB 3.3B model, but challenges remain in preserving technical accuracy. These results underscore the need for more targeted approaches to multilingual and culturally aware LLMs for African languages.