Word Embeddings in Language Models

The article discusses the development and application of word embeddings, which represent words as dense vectors in a continuous space to capture semantic relationships. It highlights methods for using pretrained embeddings and training models like Word2Vec with tools such as Gensim and PyTorch, as well as their integration into transformer models.