Text Encoding

Text encoding focuses on representing textual data in a format suitable for machine processing, aiming to capture semantic meaning and structural information for various downstream tasks. Current research emphasizes efficient and robust encoding methods, leveraging large language models (LLMs) and architectures like transformers and convolutional neural networks (CNNs), often incorporating techniques like contrastive learning and self-distillation to improve performance. These advancements are significantly impacting fields like natural language processing, information retrieval, and digital humanities, enabling improved accuracy and efficiency in tasks ranging from speech-to-text transcription to complex document analysis.