Zero Shot Cross Lingual Transfer

Zero-shot cross-lingual transfer aims to enable language models trained on one language to perform tasks in other languages without additional training data. Current research focuses on improving this transfer by enhancing multilingual alignment within pre-trained models (like mBERT, XLM-R, and Whisper), employing techniques such as layer swapping, data augmentation (e.g., back-parsing), and parameter-efficient fine-tuning. These advancements are significant because they address the scarcity of labeled data in many languages, facilitating the development of multilingual NLP applications and furthering our understanding of cross-lingual knowledge representation within large language models.