1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
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…
cs.CL2025★ 1 cited
R2T: Rule-Encoded Loss Functions for Low-Resource Sequence Tagging
Mamadou K. Keita, Christopher Homan, Sebastien Diarra
We introduce the Rule-to-Tag (R2T) framework, a hybrid approach that integrates a multi-tiered system of linguistic rules directly into a neural network's training objective. R2T's…