What is the role of embeddings in LLMs?

In Large Language Models (LLMs), embeddings play a crucial role in representing words, tokens, or characters as numerical vectors, capturing their semantic meaning and relationships. Embeddings:

• Capture Semantic Relationships: Embeddings represent words with similar meanings close together in vector space, enabling the model to understand nuances and context.
• Reduce Dimensionality: Embeddings compress high-dimensional data (e.g., vocabulary size) into lower-dimensional vectors, making computations more efficient.
• Enable Generalization: By representing words as vectors, the model can generalize to unseen words, out-of-vocabulary words, and misspelled words.
• Facilitate Analogical Reasoning: Embeddings allow the model to perform analogical reasoning, such as "king - man + woman = queen".
• Improve Performance: Embeddings have been shown to improve performance in various NLP tasks, such as language translation, text classification, and language modeling.

By leveraging embeddings, LLMs can effectively capture the complexities of language, enabling them to generate coherent and contextually appropriate text.