4 papers
When Does Data Augmentation Help? Evaluating LLM and Back-Translation Methods for Hausa and Fongbe NLP
Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra
Data scarcity limits NLP development for low-resource African languages. We evaluate two data augmentation methods -- LLM-based generation (Gemini 2.5 Flash) and back-translation (…
Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe
Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra
Large language models (LLMs) are trained on data contributed by low-resource language communities, yet the linguistic knowledge encoded in these models remains accessible only thro…
Budget-Xfer: Budget-Constrained Source Language Selection for Cross-Lingual Transfer to African Languages
Tewodros Kederalah Idris, Roald Eiselen, Prasenjit Mitra
Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selectio…
Can Embedding Similarity Predict Cross-Lingual Transfer? A Systematic Study on African Languages
Tewodros Kederalah Idris, Prasenjit Mitra, Roald Eiselen
Cross-lingual transfer is essential for building NLP systems for low-resource African languages, but practitioners lack reliable methods for selecting source languages. We systemat…