Cross Lingual Continual

Cross-lingual continual learning focuses on developing large language models (LLMs) that can efficiently and effectively learn new languages sequentially, without forgetting previously acquired linguistic knowledge. Current research emphasizes strategies to mitigate "catastrophic forgetting," often employing techniques like adjusted learning rates and parameter-efficient fine-tuning, alongside investigations into optimal data-parameter scaling and the impact of vocabulary expansion. This field is crucial for building truly multilingual AI systems capable of handling the ever-increasing volume and diversity of language data, impacting both fundamental AI research and the development of practical applications like machine translation and cross-lingual information retrieval.