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How do embeddings work?

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Summary

Embeddings are a powerful machine learning tool that can be used to represent complex information as a series of numbers. They capture relationships between concepts, images, documents, or any other data, where similar items have closer numbers and more distant items have more different numbers. Word embeddings and image embeddings are two examples of the use of embeddings, and they enable fast semantic search, realistic image generation, and the grounding of large language models in truth. This article provides an overview of how embeddings work, explores the use of word embeddings and image embeddings in more detail, and explains how they are used to prevent hallucination in large language models.

Q&As

What is an embedding and how does it work?
An embedding is a short list of numbers (floating point numbers) that represents a higher dimensional concept. An encoder takes an input from a high dimensional space (such as a sentence, image, or snippet of a song) and compresses it into a smaller dimension while retaining as much of its original meaning as possible. The compressed vector is called an embedding or latent representation of the original input.

What are some applications of embeddings?
Applications of embeddings include fast semantic search, realistic image generation, and grounding large language models in truth.

How do Word2Vec and GloVe generate word embeddings?
Word2Vec and GloVe generate word embeddings by analyzing word usage in large datasets. Word2Vec works by training a shallow neural network on a large corpus of text. The model is shown a word along with its context words within a sentence, and learns to predict the central word from its context. GloVe works by training a neural network to predict words from their contexts, and obtaining vector representations of words that capture their semantic meanings.

What is CLIP and how does it enable image generation?
CLIP (Contrastive Language-Image Pre-Training) is an encoder that was trained on a massive dataset of 400 million image-text pairs to predict if an image and text complemented each other. CLIP embeds text and images in the same embedding space, enabling you to compare images to words and vice-versa. This enables text-driven image synthesis, where images can be generated from scratch using only text as a guide.

How can embeddings be used to prevent hallucination in large language models?
Vector retrieval can be used to prevent hallucination in large language models. Vector retrieval works by using text embeddings to ground the model in factual knowledge. When a prompt could lead the model to hallucinate, vectors for key terms in the prompt are retrieved from a vector database. The model then generates a response that accounts for these factual knowledge vectors, producing something grounded and truthful.

AI Comments

👍 This article provides an incredible explanation of embeddings and how they work. The author provides great examples and resources for further learning.

👎 This article may be too complex and technical for readers without a background in computer science.

AI Discussion

Me: It's an article about embeddings and how they work. It explains how they're used in machine learning algorithms like semantic search, image generation, and large language models.

Friend: Interesting! What implications does it have?

Me: Well, it shows how powerful embeddings can be for extracting meaningful information from data. In particular, they enable algorithms to explore relationships and meanings that would otherwise be obscured by surface form. They also enable technologies like text-driven image synthesis and vector retrieval to mitigate hallucination in language models.

Action items

Experiment with text embeddings using SentenceTransformers and the all-MiniLM-L6-v2 model.
Explore image embeddings with OpenAI's CLIP encoder and create a text-driven image synthesis project.
Research vector retrieval and how it can be used to mitigate hallucination in large language models.

Technical terms

Embeddings: A short list of numbers (specifically floating point numbers) that represents a higher dimensional concept.
Encoder: Takes an input from a high dimensional space (such as a sentence, image, or snippet of a song) and compresses into a smaller dimension while retaining as much of its original meaning as possible.
Vector: A list of numbers in math.
Dimension: The size of a vector.
Semantic Search: Finding results that match the meaning and intent behind user queries, not just the keywords.
Word2Vec: A shallow neural network trained on a large corpus of text.
GloVe: A model that produces word embeddings by analyzing word usage in large datasets.
Contrastive Learning: Maximizing agreement between embeddings of complementary image-text pairs while minimizing agreement between non-matching pairs.
CLIP: Contrastive Language-Image Pre-Training.
Hallucination: Generating plausible but untrue or nonsensical text.
Vector Retrieval: Using text embeddings to ground the model in factual knowledge.