Zero Shot Multilingual

Zero-shot multilingual models aim to perform tasks across multiple languages without explicit training on each language pair, leveraging pre-trained multilingual encoders and techniques like prompt engineering and data augmentation. Current research focuses on mitigating issues like off-target translations (incorrect language output) and improving performance through probability calibration and cross-lingual consistency regularization, often employing transformer-based architectures. These advancements are significant because they address the scarcity of resources for low-resource languages and enable efficient development of multilingual applications in areas like machine translation and speech synthesis.