Embeddings in LLMs are numerical representations of words, phrases, or sentences that capture their meaning and relationships. Instead of processing raw text, LLMs convert each word or token into a vector of numbers. These embeddings allow the model to understand semantic relationships, such as synonyms or contextual similarities. For instance, the words "cat" and "feline" might have similar embeddings because they share similar meanings.
The embedding layer is a crucial component of LLMs. It maps each token to a high-dimensional vector space, where distances between vectors represent linguistic or semantic relationships. This helps the model perform tasks like identifying relevant information in a query or generating coherent responses in conversation.
Developers can also use embeddings for various downstream tasks, such as clustering similar documents or searching for related content. Pre-trained embedding models, like OpenAI’s text-embedding-ada-002, are widely used in applications beyond LLMs, including recommendation systems and semantic search.