Embeddings solve one of the primary challenges in enabling large language models to understand input, such as text or images, and contextualize them. Computers operate using numbers, not words or images.

For generative AI to function effectively, human inputs need to be converted into a format that machines can process, allowing them to identify relevant information. This relevant information is then used to generate natural-sounding responses or contextually accurate images, for example.

As the conversion method, embeddings are fundamental to operating large language models (LLMs).

What are embeddings?

Embeddings, meaning a way to represent text as a list of numbers, turn human inputs into a form that a computer can process. This lets you compare text—be it single words, sentences, paragraphs, or even entire documents—by “converting these real-world objects into complex mathematical representations that capture inherent properties and relationships between real-world data,” says AWS.

This process powers the applications we interact with daily, including search engines, product recommendation engines, social media content moderation, and even your email spam filter.

Why are embeddings important?

Embeddings provide an elegant, standardized interface for learning how real-world data is represented while retaining the semantic and syntactic relationships between that data. Using embeddings in machine learning helps to reduce data dimensionality, which in turn reduces the computing resources and time required to process the raw data. Depending on the use case, embeddings can reduce the volume of data that is retrieved, saving on resources. However, the real power of embeddings is that when we are running comparisons for embedded search prompts against vector databases, we only retrieve contextually relevant data.

Embeddings also enable efficient training in neural networks and improve the training data quality as well as the model’s ability to generalize. It’s an important way to enable and deliver new deep learning and generative AI applications when applied in neural network architectures. By using embeddings to transform high-dimensional and categorical data into continuous vector representations, data scientists enable semantic representation, dimensionality reduction, improved generalization of models, effective visualization, and efficient training in neural networks. This can in turn enable:

1. Semantic search: As embeddings can capture the meaning of a text beyond keyword-matching, they can help surface results based on the context of a query and not just the raw words being used.

2. Embedding clustering: The process of grouping similar documents into clusters to discover emerging patterns in a collection of documents without a specific programming request can help simplify the analysis of trends and insights.

3. Classification: Where clustering is an unsupervised learning algorithm done without interaction, classification is a supervised learning algorithm where the model can be told what sorts of groups or classes the dataset should be sorted into—powering content moderation, intent classification for customer support, and more.

4. Anomaly detection: Using embeddings to analyze data can help an LLM to detect potential outliers in a dataset, or even create a specific app to detect anomalies. This could be deployed to help detect insider threats.

Benefits of embeddings

The process of embedding results in a lot of benefits, especially for enterprises.

Improved data quality

The process of using embeddings can improve data quality, due to the structured definition of the request. A by-product of this can be reducing noise, removing outliers, and capturing semantic relationships. This is hugely important in natural language processing due to the need for contextual relevance.

Reduced need for manual data labeling

Automated embeddings can also reduce the need for manual data labeling, saving time and resources as well as freeing up employees for more strategic work.

Reduced costs

From a technology standpoint, embeddings reduce computational resources and costs by representing high-dimensional data in a lower-dimensional space—making the model more sustainable in the long term. The improvements in model performance are represented through benefits like collaborative filtering and improved recommendation systems.

Larger and more diverse datasets

In certain use cases, embeddings may be used to aggregate data, creating a larger, more diverse dataset. In turn, this can help to reduce bias in training data, by providing a more nuanced understanding of the relationships and patterns in data. The result is improved quality and accuracy of larger volumes of data.

Embeddings: How we turn “human” language into “computerese”

The evolution of technology has generally relied on what’s called structured data. This is highly organized, well-labeled, easily mapped data that’s simple for machines to decipher using SQL, a programming language developed by IBM in 1974 specifically for the task. This data is usually stored in a relational database management system (RDBMS), which is why it’s sometimes also referred to as relational data. Structured data generated by machines can include things like scientific data, digital surveillance, and satellite imagery data, such as weather and geographic forms.

Yet much of the data generated by businesses these days is unstructured. This is information that’s not arranged according to a preset data model or schema, and so can’t be stored, processed, or analyzed by conventional data tools and methods. Within a business, think of all the PowerPoint presentations, emails, strategies, reports, videos, audio files, spreadsheets, training programs, photos, web pages, customer interactions, employee handbooks—and more—that is generated and requires storing. Up to 90% of data generated and collected by organizations is unstructured.

Imagine what could happen if that unstructured data could be harnessed. McKinsey research estimates that using generative AI could add the equivalent of between $2.6 trillion and $4.4 trillion of value to business annually if CIOs and CTOs are ready. This unstructured data contains a wealth of information that can be used to guide business decisions, so it’s important that we can convert it from “human” language into “computerese” for computational analysis. This is done through the mechanism of tokenization and computing mathematical vectors.

What are vectors in machine learning embeddings?

In machine learning, vectors are mathematical representations of data, encoding the attributes and meaning of data entities. These entities could be partial words, whole words, simple phrases, or even complete sentences. In relation to LLMs, vectors are created during the embedding process when text data is converted into a numerical schema that the LLM can understand and process.

Tokenization is the first step. During tokenization, text strings are broken down into smaller units. These tokens are then mapped to a vector, which acts as a compact representation of each token. Vectors are constructed based on relationships between words, as well as their context and meaning.

Embeddings are a specific type of vector, and vectorized embeddings can be used as an effective and efficient way to encode and compare data. They can capture inter-word semantics based on the nuanced relationships between words, as well as context and language variants such as analogies or synonyms. It is vectors that signpost these kinds of relationships between tokens.

How do embeddings work?

It is possible to embed multiple different data types, including images. Let's hone in on specifically the text embeddings use-case.

First, the user inputs a request to the model. This text is converted into tokens by calling on an Embed endpoint, taking the text as input, and returning embeddings as tokenized output. For most LLMs, the embedding process is completed through the use of byte pair encoding. This is an algorithm for encoding strings of text into tables. LLMs use a modified byte pair encoding algorithm to vectorize data.

Each token is assigned an embedding, which is then refined to more closely fit the information and data within the model that was learned during its training. This process results in the context of a word (both current and historical) being evaluated, leading to more accurate responses. In natural language usage, the same word can have multiple meanings, or could mean something different in a certain context. Let’s use the word tender as an example. In one context it can be an adjective indicating something is soft; in another, it’s a verb describing a proposal or quote; in yet another, it’s an adverb describing emotional behavior. Vectorizing tokens as part of the embedding process uncovers the underlying context of a word to add semantic meaning

The embed endpoint called on during this process can be connected to different sizes of LLM; the size you need will depend on how densely contextual the input data is. Additional pieces of information assigned to a token may require an extra dimension to be stored, but not always. As the number of dimensions available in the model increases, so too does the representational power. Using a technique known as Principle Components Analysis (PCA), the number of dimensions in an embedding can be reduced while retaining as much information as possible, saving computational power.

By this end stage, the embeddings process has converted tokenized text into fixed-dimensional vectors that have encoded the semantic meaning. Once the embeddings have been processed, they can be used for various purposes, such as clustering, search and retrieval, knowledge representation, or even input for machine language models.

The embedding process helps the model to predict the most likely next word in a text so it can create a natural language answer to the user’s prompt.

So what are embedding models?

Technology evolves in such a way that there’s rarely a single means of doing things, and embeddings are no different: there are many different embedding models used in machine learning applications, including BERT, Word2Vec, and Cohere Embed enable text and images to be classified or turned into embeddings. Whichever model you use, the fundamental end result should always be the same: a document or visual that can be represented numerically through vectors, encoding semantics and context as well as the actual characters.

How embedding models work

Embeddings work by taking data as input - it could be text, images, or any other form of data - which is then processed by the embedding model, and mapped into lower-dimensional space. The result is related data being clustered together, and the output is a vector of numbers that represent this data.

How embedding models are used

Embedding models suit a certain set of uses, such as where there is a need to cluster similar data together or uncover similarities between data. Applications for embedding models include:

1. Automated recommendation tools: An embedding model can be used to find similarities between the preferences of a user and products, to recommend purchases intelligently.

2. Image processing: Raw image data can be processed by an embedding model, and converted to vector space for fast image search or classification.

3. Natural language processing: Embedding models can be used to uncover relationships in the contents of unstructured text data. This provides a valuable tool for a range of text manipulation use cases, such as translation or sentiment analysis.

What to consider when using embeddings

While embeddings could be regarded as an essential tool for many natural language processing (NLP) tasks, you’ll want to carefully consider if it’s the right process for your endeavors.

Historically, a challenge was that traditional embeddings couldn’t capture the multiple meanings some words have, as they provided a single vector representation per word. Present-day contextual embeddings, generated by models like transformers, address this by encoding words based on context. However, these embeddings introduce other challenges, such as high dimensionality and computational complexity, especially for detailed corpora. This can result in a large memory footprint and slower computation times, increasing IT costs. Additionally, richer training corpora may require embeddings with greater precision and capacity, exacerbating these challenges.

However, it’s vital to remember that these considerations should be weighed against the numerous benefits and value embeddings provide.

Conversely, there can be issues of data sparsity. What if a particular word used as input was not part of the training corpus? Then the embedding would be inaccurate, which can lead to poor model performance. Using retrieval-augmented generation (RAG) with your LLM can help mitigate this issue by introducing external, contextually relevant data into the generation process, improving the model's ability to handle queries with rare or unknown terms effectively.

The very nature of language can have its limitations, too. When the meaning of a word changes over time, known as semantic drift, it can result in inaccurate results when using the embeddings for prediction tasks. On top of that, like any aspect of machine learning, embeddings can run the risk of introducing biases to models or misinformation in the training data, meaning any data ingested needs to be clean and handled appropriately at the very start.

Embeddings: The top takeaways

1. Embeddings are fundamental to LLMs as they transform natural language into mathematical representations that algorithms can efficiently process. This transformation occurs through the dual processes of tokenization and vectorization.

2. Embeddings can be a valuable tool in helping to uncover insights contained with the unstructured data within an enterprise—such as presentations, spreadsheets, media files, instant messages, and emails—into structured data suitable for RAG in LLMs, enhancing the efficiency and accessibility of information gathering in the workforce.

3. Embeddings also help optimize computational resources and reduce costs by representing high-dimensional data in a lower-dimensional space. The result is more efficient and performant processing of data.

4. However, embeddings can result in a significant memory footprint and slower computation times. If not handled carefully, they may also carry biases or misinformation from the training data, leading to inaccurate predictions. Additionally, developers must be cautious of semantic drift, where the meaning of words may evolve or change over time.

Expedite implementing your own Embed flow by consulting this Cohere cookbook to get started right away.

FAQS about embeddings

What are embeddings in machine learning?

In machine learning, embeddings are a vector representation of data. They are used to map high-dimensional data, such as images and text, into a lower-dimensional space while preserving the original message in the data. Embeddings help by representing complex data in a more structured way.

How are embeddings used in large language models?

Embeddings are the foundation of how LLMs process text, turning words into numbers so a computer can understand them. Each word or part of a word gets a unique vector, which is like a point in a multi-dimensional space. Words with similar meanings end up closer together in this space.

The large language model uses these embeddings to learn patterns, relationships, and context between words.

This transformation through the model enriches them with context and knowledge derived from the LLM's training data. This enrichment enables the embeddings to carry more nuanced, task-specific information.

How do multimodal embeddings work?

Multimodal embeddings are a way to combine information from different types of data, like text, images, or audio, into a shared "language" based on meaning that a computer can understand.

Each type of data is turned into its own vector (a list of numbers), and these vectors are placed in the same space. The goal is to make related things—like a picture of a cat and the word "cat"—end up close together in that shared space. This helps the model connect and understand relationships between different types of information, like matching captions to images or generating text from a video.

This allows the model to provide richer, more diverse results, capturing more context and meaning than text alone. For example, it can link a description of an object to its image or sound, making it more versatile and accurate in understanding and retrieving information.

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