Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a challenge yet to be fully addressed. A distinctive characteristic of MLLMs is their high-quality translation capabilities, indicating an acquired proficiency in aligning between languages. This study explores how to enhance the zero-shot performance of MLLMs in non-English languages by leveraging their alignment capability between English and non-English languages. To achieve this, we first analyze the behavior of MLLMs when performing translation and reveal that there are large magnitude features that play a critical role in the translation process. Inspired by these findings, we retain the weights associated with operations involving the large magnitude features and prune other weights to force MLLMs to rely on these features for tasks beyond translation. We empirically demonstrate that this pruning strategy can enhance the MLLMs' performance in non-English language.

本研究针对多语言大型语言模型（MLLMs）在非英语语言中的表现差距进行探讨，提出通过分析多语言模型的翻译行为来加强其零-shot学习能力。研究表明，保留与大幅度特征相关的权重并修剪其他权重的方法，可以显著提升MLLMs在非英语语言任务中的表现，展示了该方法的潜在影响。

为多语言推理修剪多语言大型语言模型