In the context of machine learning and natural language processing (NLP), an embedding is a technique used to represent words, phrases, or other types of data in a high-dimensional vector space. These vectors capture semantic relationships between words, allowing the model to understand meaning, context, and associations between terms more effectively.
By converting words or pieces of content into dense, continuous vectors, embeddings enable a large language model (LLM) to process text more efficiently. This approach aids in tasks like meaning interpretation, translation, and content generation, as the vectors encode information about the words’ meanings and how they relate to one another.
Popular embedding techniques include Word2Vec, GloVe, and transformer-based models like BERT, which use embeddings to understand language and produce relevant outputs. Embeddings are a core component of modern NLP systems, improving their ability to handle complex linguistic tasks.