Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability

• URL: http://arxiv.org/abs/2407.00454v2
• Date: Tue, 17 Sep 2024 10:04:22 GMT
• Title: Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability
• Authors: Ryokan Ri, Shun Kiyono, Sho Takase,
• Abstract summary: Self-Translate-Train is a method that lets large language models translate training data into the target language and fine-tunes the model on its own generated data. By demonstrating that Self-Translate-Train outperforms zero-shot transfer, we encourage further exploration of better methods to elicit cross-lingual capabilities of LLMs.
• Score: 31.025371443719404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot cross-lingual transfer by fine-tuning multilingual pretrained models shows promise for low-resource languages, but often suffers from misalignment of internal representations between languages. We hypothesize that even when the model cannot generalize across languages effectively in fine-tuning, it still captures cross-lingual correspondence useful for cross-lingual transfer. We explore this hypothesis with Self-Translate-Train, a method that lets large language models (LLMs) to translate training data into the target language and fine-tunes the model on its own generated data. By demonstrating that Self-Translate-Train outperforms zero-shot transfer, we encourage further exploration of better methods to elicit cross-lingual capabilities of LLMs.

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