1 citations · 1 across the 9 of their papers we have counts for
9 papers
LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review
Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Paula Chikaodinaka Anyanwu +9
Large Language Models (LLMs) have achieved substantial progress in safety alignment, yet their safety guarantees remain significantly weaker in low-resource and multilingual settin…
Linguistically Informed Evaluation of Multilingual ASR for African Languages
Fei-Yueh Chen, Lateef Adeleke, C. M. Downey
Word Error Rate (WER) mischaracterizes ASR models' performance for African languages by combining phonological, tone, and other linguistic errors into a single lexical error. By co…
Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review
Sukairaj Hafiz Imam, Tadesse Destaw Belay, Kedir Yassin Husse +7
ASR has achieved remarkable global progress, yet African low-resource languages remain rigorously underrepresented, producing barriers to digital inclusion across the continent wit…
The Rise of AfricaNLP: A Survey of Contributions, Contributors, Community Impact, and Bibliometric Analysis
Tadesse Destaw Belay, Kedir Yassin Hussen, Sukairaj Hafiz Imam +11
Natural Language Processing (NLP) is undergoing constant transformation, as Large Language Models (LLMs) are driving daily breakthroughs in research and practice. In this regard, t…
The State of Large Language Models for African Languages: Progress and Challenges
Kedir Yassin Hussen, Walelign Tewabe Sewunetie, Abinew Ali Ayele +3
Large Language Models (LLMs) are transforming Natural Language Processing (NLP), but their benefits are largely absent for Africa's 2,000 low-resource languages. This paper compara…
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions
Sukairaj Hafiz Imam, Babangida Sani, Dawit Ketema Gete +6
Automatic Speech Recognition (ASR) technologies have transformed human-computer interaction; however, low-resource languages in Africa remain significantly underrepresented in both…