Pre Trained Text

Pre-trained text encoders are foundational models in natural language processing, aiming to learn general-purpose text representations from massive datasets for downstream tasks. Current research focuses on improving efficiency (e.g., through lightweight models and sparse training), enhancing zero-shot capabilities (e.g., via prototype shifting and contrastive learning), and addressing limitations like domain shift and knowledge gaps (e.g., by incorporating generative LLMs and visual information). These advancements are significantly impacting various fields, enabling improved performance in tasks ranging from image retrieval and generation to intent classification and medical image analysis.