Kuwain 1.5B: An Arabic SLM via Language Injection
Authors: name: Khalil Hennara, name: Sara Chrouf, name: Mohamed Motaism Hamed, name: Zeina Aldallal, name: Omar Hadid, name: Safwan AlModhayan
Organization: Misraj Ai

Enhancing existing models with new knowledge is a crucial aspect of AI
development. This paper introduces a novel method for integrating a new
language into a large language model (LLM). Our approach successfully
incorporates a previously unseen target language into an existing LLM without
compromising its prior knowledge. We trained a tiny model with 1.5 billion
parameters named Kuwain by injecting the Arabic language into a small
open-source model mainly trained in English. Our method demonstrates
significant improvements in Arabic language performance, with an average 8%
improvement across various benchmarks, while retaining the model's existing
knowledge with a minimum amount of the original model's data. This offers a
cost-effective alternative to training a comprehensive model in both English
and Arabic. The results highlight the potential for efficient, targeted
language model expansion without extensive retraining or resource-intensive
processes.