Large language models demonstrate reasonable multilingual abilities, despite predominantly English-centric pretraining. However, the spontaneous multilingual alignment in these models is shown to be weak, leading to unsatisfactory cross-lingual transfer and knowledge sharing. Previous works attempt to address this issue by explicitly injecting multilingual alignment information during or after pretraining. Thus for the early stage in pretraining, the alignment is weak for sharing information or knowledge across languages. In this paper, we propose PreAlign, a framework that establishes multilingual alignment prior to language model pretraining. PreAlign injects multilingual alignment by initializing the model to generate similar representations of aligned words and preserves this alignment using a code-switching strategy during pretraining. Extensive experiments in a synthetic English to English-Clone setting demonstrate that PreAlign significantly outperforms standard multilingual joint training in language modeling, zero-shot cross-lingual transfer, and cross-lingual knowledge application. Further experiments in real-world scenarios further validate PreAlign's effectiveness across various model sizes.

翻译：大型语言模型展现出合理的多语言能力，尽管其预训练过程主要围绕英语进行。然而，这些模型中自发的多语言对齐被证明是薄弱的，导致跨语言迁移和知识共享效果不尽如人意。先前的研究试图通过在预训练期间或之后显式注入多语言对齐信息来解决这一问题。因此在预训练的早期阶段，跨语言共享信息或知识的对齐能力较弱。本文提出PreAlign框架，该框架在语言模型预训练之前建立多语言对齐。PreAlign通过初始化模型以生成对齐词语的相似表示来注入多语言对齐，并在预训练期间使用代码切换策略保持这种对齐。在合成的英语到英语克隆场景中进行的大量实验表明，PreAlign在语言建模、零样本跨语言迁移和跨语言知识应用方面显著优于标准的多语言联合训练。在真实场景中的进一步实验也验证了PreAlign在不同模型规模下的有效性。