3 citations · 3 across the 3 of their papers we have counts for
4 papers
Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM
Everlyn Asiko Chimoto, Mostafa Elhoushi, Bruce A. Bassett
Quantization is an effective technique for reducing the storage footprint and computational costs of Large Language Models (LLMs), but it often results in performance degradation.…
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages
Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto +12
This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their l…
Very Low Resource Sentence Alignment: Luhya and Swahili
Everlyn Asiko Chimoto, Bruce A. Bassett
Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-r…
COMET-QE and Active Learning for Low-Resource Machine Translation
Everlyn Asiko Chimoto, Bruce A. Bassett
Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine…