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语言模型（MLLMs）在非英语语言上的零-shot学习性能差距问题，提出了一种利用模型在翻译中的对齐能力来增强其非英语语言性能的新方法。研究发现，与翻译过程相关的大幅度特征对模型表现至关重要，通过保留这些特征的权重并修剪其他权重，实验证明这种修剪策略能有效提升MLLMs在非英语语言上的表现。

为多语言推理修剪多语言大语言模型