Language Pretraining

Language pretraining focuses on training large language models on massive datasets to learn general language representations before fine-tuning them for specific tasks. Current research emphasizes improving model alignment across modalities (e.g., vision, speech, and text) using techniques like knowledge distillation and adapter-based fine-tuning, as well as exploring alternatives to text-based supervision, such as unimodal image training. These advancements are driving improvements in various downstream applications, including machine translation, question answering, and medical image analysis, by providing more robust and efficient foundational models.