Pre Training Objective

Pre-training objectives are crucial for establishing the foundational knowledge of large language models and other deep learning architectures, aiming to improve their performance on downstream tasks. Current research focuses on optimizing these objectives, exploring various approaches such as task-agnostic feature learning, hierarchical sentence representations, and cross-document question answering, often within transformer-based models. The effectiveness of different pre-training strategies, including the impact of linguistically-informed objectives versus more general approaches, is a key area of investigation, with implications for both the efficiency and capabilities of future AI systems. Ultimately, improved pre-training methods promise to enhance the performance and generalization abilities of models across diverse applications.