Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
This addresses the problem of language imbalance in multilingual AI models, offering an incremental but effective method to improve performance for high, medium, and low-resource languages.
The paper investigates how code-switching in pre-training data enhances multilingual capabilities in large language models, finding that scaling up synthetic code-switching data leads to significant improvements in benchmarks and representation alignment across languages.
Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. In this paper, we closely examine the reasons behind this phenomenon, focusing on the pre-training corpus. We find that the existence of code-switching, alternating between different languages within a context, is key to multilingual capabilities. We conduct an analysis to investigate code-switching in the pre-training corpus, examining its presence and categorizing it into four types within two quadrants. We then assess its impact on multilingual performance. These types of code-switching data are unbalanced in proportions and demonstrate different effects on facilitating language transfer. To better explore the power of code-switching for language alignment during pre-training, we investigate the strategy of synthetic code-switching. We continuously scale up the synthetic code-switching data and observe remarkable improvements in both benchmarks and representation space. Extensive experiments indicate that incorporating synthetic code-switching data enables better language alignment and generalizes well to high, medium, and low-resource languages with pre-training corpora of varying qualities.