AI RESEARCH
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
arXiv CS.CL
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ArXi:2410.16006v2 Announce Type: replace A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages in which the model is already proficient (usually English). Continual fine-tuning (CFT) is the process of sequentially fine-tuning an LLM to enable the model to adapt to downstream tasks with varying data distributions and time shifts. This paper focuses on the language adaptability of LLMs through.