Exploring Two-Phase Continual Instruction Fine-tuning for Multilingual Adaptation in Large Language Models (2026.findings-acl)
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| Challenge: | A key challenge for Large Language Models (LLMs) is improving their Multilingual instruction-following ability over time without deteriorating their ability in languages they already excel at, typically English. |
| Approach: | They propose a two-phase Continual Fine-tuning setup to improve a model's Multilingual adaptability by comparing an English-only LLM with a multilingual instruction dataset. |
| Outcome: | The proposed model improves on two-phase Continual Fine-tuning (CFT) setups on a multilingual instruction dataset. |
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