AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages

TL;DR

This paper introduces AfroXLMR-Comet, a compact multilingual model for low-resource languages that uses hybrid knowledge distillation with attention matching to maintain high performance while significantly reducing size.

Contribution

The paper presents a novel hybrid distillation method combining traditional knowledge distillation with attention matching, tailored for multilingual low-resource language models.

Findings

Achieves over 85% size reduction of the teacher model.

Maintains 85% of the original model's accuracy on African languages.

Demonstrates competitive performance with substantially fewer resources.

Abstract

Language model compression through knowledge distillation has emerged as a promising approach for deploying large language models in resource-constrained environments. However, existing methods often struggle to maintain performance when distilling multilingual models, especially for low-resource languages. In this paper, we present a novel hybrid distillation approach that combines traditional knowledge distillation with a simplified attention matching mechanism, specifically designed for multilingual contexts. Our method introduces an extremely compact student model architecture, significantly smaller than conventional multilingual models. We evaluate our approach on five African languages: Kinyarwanda, Swahili, Hausa, Igbo, and Yoruba. The distilled student model; AfroXLMR-Comet successfully captures both the output distribution and internal attention patterns of a larger teacher model (AfroXLMR-Large) while reducing the model size by over 85%. Experimental results demonstrate that our hybrid approach achieves competitive performance compared to the teacher model, maintaining an accuracy within 85% of the original model's performance while requiring substantially fewer computational resources. Our work provides a practical framework for deploying efficient multilingual models in resource-constrained environments, particularly benefiting applications involving African languages.