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Embedding Models

Summary

Embedding models in Large Language Models (LLMs) are algorithms designed to learn and generate embeddings, which are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space. These embeddings enable LLMs to understand and process natural language data efficiently. Embedding models are crucial for tasks such as text classification, summarization, translation, and semantic search. They can also be used to generate and transcribe audio, turn text into images or vice versa, and create code from text or vice versa.

Key Concepts

• Embeddings : Embeddings are numeric representations of non-numeric data that capture semantic meanings, allowing LLMs to determine relationships between concepts.

• Tokenization : Tokenization is the process of converting text into individual words, subwords, or tokens that the model can understand.

• Attention Mechanism : Attention mechanisms in LLMs allow the model to weigh the importance of different words or phrases, enabling it to focus on the most relevant information.

• Pre-training : Pre-training is the process of training an LLM on a large dataset to learn general language patterns and relationships between words.

• Transfer Learning : Transfer learning involves fine-tuning a pre-trained model on a smaller, task-specific dataset to achieve high performance on that task.

References

URL Name	URL
Embeddings 101	https://datasciencedojo.com/blog/embeddings-and-llm/
Choosing Embedding Models	https://www.mongodb.com/developer/products/atlas/choose-embedding-model-rag/
LLM Embeddings	https://llm.datasette.io/en/stable/embeddings/index.html
Understanding LLM Embeddings	https://irisagent.com/blog/understanding-llm-embeddings-a-comprehensive-guide/
Embeddings in .NET	https://learn.microsoft.com/en-us/dotnet/ai/conceptual/embeddings

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Ingesting in LLM