Code-Switching Curriculum Learning for Multilingual Transfer in LLMs

• URL: http://arxiv.org/abs/2411.02460v1
• Date: Mon, 04 Nov 2024 06:31:26 GMT
• Title: Code-Switching Curriculum Learning for Multilingual Transfer in LLMs
• Authors: Haneul Yoo, Cheonbok Park, Sangdoo Yun, Alice Oh, Hwaran Lee,
• Abstract summary: Large language models (LLMs) exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages. Inspired by the human process of second language acquisition, we propose code-switching curriculum learning (CSCL) to enhance cross-lingual transfer for LLMs. CSCL mimics the stages of human language learning by progressively training models with a curriculum consisting of 1) token-level code-switching, 2) sentence-level code-switching, and 3) monolingual corpora.
• Score: 43.85646680303273
• License:
• Abstract: Large language models (LLMs) now exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages due to the imbalance in pre-training data. Inspired by the human process of second language acquisition, particularly code-switching (the practice of language alternation in a conversation), we propose code-switching curriculum learning (CSCL) to enhance cross-lingual transfer for LLMs. CSCL mimics the stages of human language learning by progressively training models with a curriculum consisting of 1) token-level code-switching, 2) sentence-level code-switching, and 3) monolingual corpora. Using Qwen 2 as our underlying model, we demonstrate the efficacy of the CSCL in improving language transfer to Korean, achieving significant performance gains compared to monolingual continual pre-training methods. Ablation studies reveal that both token- and sentence-level code-switching significantly enhance cross-lingual transfer and that curriculum learning amplifies these effects. We also extend our findings into various languages, including Japanese (high-resource) and Indonesian (low-resource), and using two additional models (Gemma 2 and Phi 3.5). We further show that CSCL mitigates spurious correlations between language resources and safety alignment, presenting a robust, efficient framework for more equitable language transfer in LLMs. We observe that CSCL is effective for low-resource settings where high-quality, monolingual corpora for language transfer are hardly available.

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