arXiv:2605.29414v1 Announce Type: cross Abstract: Recent studies have shown that code-switching data (CSD), in which multiple languages are mixed within the same context, can improve cross-lingual transfer and multilingual alignment in large language models (LLMs). However, existing studies primarily focus on bilingual transfer between English and a target language, leaving multilingual settings involving three or more languages largely unexplored. In this work, we investigate multilingual code-switching instruction tuning across four languages: English, Japanese, Korean, and Chinese. We evalu
The rapid development and deployment of LLMs necessitate continuous innovation in training methodologies, particularly in multilingual contexts as AI applications expand globally.
Improving multilingual capabilities of LLMs through code-switching is critical for expanding AI's global utility and reducing biases inherent in English-centric training.
The research suggests a more effective method for training multilingual LLMs, moving beyond simple bilingual transfer to more complex code-switching scenarios.
- · AI developers
- · Multilingual businesses
- · Non-English speaking markets
- · LLM users
- · Monolingual AI solutions
Increased performance and efficiency of LLMs in mixed-language environments.
Accelerated adoption of AI in diverse linguistic regions and for cross-cultural communication.
Potential for new AI applications that seamlessly integrate multiple languages, fostering greater global connectivity and reducing language barriers in digital interactions.
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Read at arXiv cs.AI