Language Model Pre Training

Language model pre-training aims to create powerful language models by training them on massive text datasets before fine-tuning for specific downstream tasks. Current research emphasizes improving data efficiency through better data selection and more effective sequence construction methods, exploring diverse architectures beyond purely autoregressive models, and investigating the impact of different training objectives and bidirectionality on model performance. These advancements are crucial for building more robust and efficient language models, impacting various NLP applications and furthering our understanding of how these models learn and generalize.