The pruning strategy can be optimized by systematically experimenting with various pruning ratios (α) to identify the optimal balance between model size reduction and performance retention. By varying the pruning ratio, researchers can assess how different levels of weight removal impact the model's ability to leverage large magnitude features, which are crucial for enhancing zero-shot performance in non-English languages. A lower pruning ratio may retain more features, potentially preserving performance, while a higher ratio could lead to significant performance gains in terms of efficiency and speed, albeit at the risk of degrading accuracy.
Additionally, the number (N) and quality of few-shot translation demonstrations (n) play a critical role in the effectiveness of the pruning strategy. By increasing the number of bilingual sentence pairs used to construct these demonstrations, the model may better capture the nuances of language alignment, leading to improved performance. Furthermore, focusing on the quality of these demonstrations—ensuring they are representative and diverse—can enhance the model's ability to generalize across languages. This dual approach of optimizing pruning ratios alongside the number and quality of demonstrations can lead to a more robust pruning strategy that maximizes multilingual capabilities while maintaining performance in English.