Token Embeddings

Token embeddings, numerical representations of words or sub-word units, are fundamental to many natural language processing (NLP) models, aiming to capture semantic meaning and contextual information. Current research focuses on improving embedding efficiency and robustness, exploring techniques like decoupled embeddings, reinforced positional embeddings, and novel pooling strategies within transformer architectures to reduce computational costs and enhance performance across diverse languages and domains. These advancements are crucial for building more efficient and effective language models, impacting applications ranging from machine translation and question answering to speech recognition and information retrieval.