Unlocking the Potential of Model Merging for Low-Resource Languages

Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised finetuning (SFT). However, this CT-then-SFT approach struggles with limited data in the context of low-resource languages, failing to balance language modeling and task-solving capabilities. We thus propose a new model merging solution as an alternative for lowresource languages, combining models with distinct capabilities into a single model without additional training. We use model merging to develop task-solving LLMs for lowresource languages without SFT data in the target languages. Our experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data. Observing performance saturation in model merging with increasingly more training tokens, we further analyze the merging process and introduce a slack variable to the model merging algorithm to mitigate the loss of important parameters, thereby enhancing model performance. We hope that model merging can benefit more human languages suffering from data scarcity with its higher data efficiency.