How Embedding Power Large Language Models

Lets talk about embedding a bit another interesting area

In LLMs (Large Language Models), embedding are numerical representations of words, phrases, or sentences that capture their meaning and context. Embeddings are used to represent text in a way that can be processed by machine learning algorithms.

There are two main types of embeddings:

• Word embeddings:?Word embeddings represent individual words as vectors of numbers. The numbers in a word embedding vector represent the meaning of the word, and the relationships between words.
• Sentence embeddings:?Sentence embeddings represent entire sentences as vectors of numbers. Sentence embeddings are created by averaging the word embeddings of the words in the sentence.

Embeddings are used in a variety of NLP (Natural Language Processing) tasks, including:

• Text classification:?Embeddings can be used to classify text into different categories, such as news, fiction, or spam.
• Text summarization:?Embeddings can be used to summarize text into a shorter, more concise version.
• Question answering:?Embeddings can be used to answer questions about text.
• Machine translation:?Embeddings can be used to translate text from one language to another.

Happy Learning!