1 citations · 2 across the 4 of their papers we have counts for
4 papers
InstructLR: A Scalable Approach to Create Instruction Dataset for Under-Resourced Languages
Mamadou K. Keita, Sebastien Diarra, Christopher Homan +1
Effective text generation and chat interfaces for low-resource languages (LRLs) remain a challenge for state-of-the-art large language models (LLMs) to support. This is mainly due…
R2T: Rule-Encoded Loss Functions for Sequence Tagging in Low-Resource Languages
Mamadou K. Keita, Christopher Homan, Sebastien Diarra
We introduce Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource languages. R2T encodes lexical, morphol…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra +6
Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires si…