LLM Embeddings

Embeddings are a crucial component in Large Language Models (LLMs), representing words or tokens as dense vectors in a high-dimensional space. These numerical representations capture semantic relationships and meanings, enabling LLMs to process and understand language more effectively.

The Embedding Process

1. Tokenization: Converting text into tokens - This step precedes embedding but is essential for the process

2. Vector Generation: Creating numerical representations - Each token is assigned a unique vector - Vectors typically have hundreds or thousands of dimensions

3. Semantic Encoding: Capturing meaning in vector space - Similar words or concepts have similar vector representations - Relationships between words are reflected in vector operations

4. Contextual Embeddings: Considering surrounding context - Modern LLMs often use contextual embeddings - The same word can have different embeddings based on its context

Example: Input: "The bank is by the river" and "I need to bank my check" The word "bank" would have different embeddings in each sentence

5. Embedding Space: The high-dimensional space where embeddings exist - Allows for complex relationships and analogies - Enables mathematical operations on language (e.g., king - man + woman ≈ queen)

Embedding Techniques and Models

• Word2Vec: One of the earliest popular embedding techniques
• GloVe (Global Vectors): Combines global matrix factorization and local context window methods
• FastText: Extends Word2Vec by using subword information
• BERT Embeddings: Contextual embeddings from the BERT model
• GPT Embeddings: Embeddings generated by GPT models, also contextual

By using embeddings, LLMs can effectively represent the complexities of language in a format that's suitable for deep learning models. This leads to more nuanced understanding and generation of human-like text.

SentenceTransformers

SentenceTransformers is a Python framework for sentence, text and image embeddings. It is built on top of PyTorch and Transformers. This is a very powerful tool to create embeddings for your LLMs.