Language Transformer

Language transformers are deep learning models designed to process sequential data, primarily text and increasingly images, by leveraging self-attention mechanisms to capture long-range dependencies. Current research focuses on improving efficiency (e.g., through pruning, quantization, and novel training strategies), enhancing performance on diverse tasks (including few-shot learning, visual grounding, and interactive text environments), and understanding the internal representations and optimization dynamics of these models. This work is significant because it pushes the boundaries of natural language processing and multimodal understanding, leading to advancements in conversational AI, image captioning, and other applications requiring sophisticated language and vision capabilities.