An embedding converts complex information like words or images into numerical codes that capture their meaning, allowing machines to
learn concepts by transforming them into mathematical language.
During
AI model training, through
natural language processing, models convert
tokens into embeddings, transforming them into numerical
vectors. This transformation allows converting abstract concepts such as language or visual images into mathematical
vectors, enabling complex operations to extract information
patterns and find semantic or visual relationships. For example, related words like "king" and "queen" will have similar and close embeddings in this mathematical space.
Thus, in the case of
large language models, embeddings allow them to understand semantic relationships, enabling tasks such as translation, text generation, or answering questions. A model like ChatGPT uses embeddings to understand context and generate coherent responses.