Language Modeling Task

Language modeling tasks focus on training computational models to predict the probability of sequences of words, enabling applications like text generation and translation. Current research emphasizes improving model efficiency and performance, particularly through exploring novel architectures like state-space models and loop-residual networks, as well as optimizing existing transformers via techniques such as pruning, knowledge distillation, and prompt engineering. These advancements aim to reduce computational costs while enhancing accuracy and addressing limitations in handling long sequences and incorporating multimodal information, ultimately impacting various fields from natural language processing to user interface design.