What are Embeddings and How Do They Help AI Process Words?

Large language models, or LLMs, are AI systems that work with human language. They can write stories, answer questions, and translate text. For these models to do their job, they need a special way to process words. This is where embeddings come into play. Embeddings are a key part of how these AI systems make sense of language.

What Exactly Are Embeddings?

Embeddings are basically a way to turn words, phrases, or even whole sentences into lists of numbers. Think of each word getting its own unique numerical code. This code is not just random numbers. It is carefully designed so that words with similar meanings get similar sets of numbers. So, "happy" and "joyful" would have number lists that are close to each other, while "happy" and "car" would have very different number lists. These number lists are often called vectors. These vectors exist in a high-dimensional space, meaning they have many numbers in their list, sometimes hundreds or even thousands. This numerical representation is what AI models use to work with language.

Why Do Computers Need Embeddings for Language?

Computers are great with numbers, but they do not naturally grasp the meaning of words like humans do. You cannot just feed a computer the word "apple" and expect it to know you mean a fruit or something else, without some help. Raw text is just a sequence of characters to a machine. To perform complex tasks like translation, summarization, or question answering, the AI needs a way to "feel" the relationships between words. Embeddings provide this numerical representation that computers can process and learn from. They bridge the gap between human language and the mathematical world of computers, making text processable for calculations.

How Are Embeddings Created?

Creating embeddings involves training a model on a massive amount of text. The model learns by looking at how words are used together in sentences. For example, it might notice that "king" often appears with words like "queen," "royal," and "throne." Similarly, "apple" might appear with "eat," "fruit," "tree," or "pie."

During this training process, the model adjusts the number lists for each word. The goal is to arrange these number lists in a way that captures these relationships. Words that frequently appear in similar contexts will end up with number lists that are mathematically "close." This closeness can be measured using techniques like calculating the distance or angle between the vectors. The process is complex, but the outcome is a rich, numerical representation for each word in the model's vocabulary.

Embeddings: Capturing Meaning and Relationships

The magic of embeddings is that they do not just give words a number; they capture some of the word's meaning and how it relates to other words. Because "happy" and "glad" are used in similar ways in sentences, their embedding vectors will be close together in this numerical space. "Sad" and "unhappy" will also be close to each other, but further away from "happy."

This even extends to more complex relationships. For instance, the relationship between "man" and "woman" might be numerically similar to the relationship between "king" and "queen." This means you could potentially perform "vector arithmetic" such as: "king" - "man" + "woman" results in a vector close to "queen". While not always perfectly precise, this shows how embeddings store semantic information. This ability allows LLMs to process nuances, context, and analogies in language.

Embeddings in Action with Large Language Models

When you ask an LLM a question, the first step is often to convert your words into embeddings. The LLM then uses these numerical representations to find relevant information, process the context of your query, and generate a coherent response.

For instance, if you ask, "What is the weather like in London?", the words "weather," "like," and "London" are turned into their respective embedding vectors. The LLM uses these vectors to process your request. It can find documents or internal knowledge related to weather and London because their embeddings will be close to concepts associated with these terms. When generating an answer, the LLM also works with embeddings, choosing words whose embeddings fit the context and meaning it wants to convey. This use of embeddings is critical for tasks like text generation, where the model needs to pick appropriate subsequent words.

The Advantage of Numbers for AI

Once words are converted into these numerical vectors (embeddings), all sorts of mathematical operations can be performed on them. AI models, particularly those based on neural networks, are designed to work with numbers. They can find patterns, make calculations, and learn from these numerical inputs much more effectively than they could from raw text.

This numerical format allows the model to compare words, measure similarity, group related concepts, and make predictions. For example, a model can determine if two sentences have similar meanings by comparing their combined embeddings. This numerical foundation is what enables LLMs to perform sophisticated language tasks with greater efficiency and accuracy.

How Embeddings Are Learned

Embeddings are not pre-programmed by humans with specific values for each word. Instead, they are learned automatically by the AI model. This learning happens by feeding the model vast quantities of text—books, articles, websites, and more.

The model is typically given a task, such as predicting the next word in a sentence or filling in a missing word. As it tries to perform this task and gets corrected when it makes mistakes, it gradually adjusts the embedding values. Over time, through countless examples, the embeddings evolve to effectively represent the words and their relationships in a way that helps the model succeed at its given language tasks. Popular algorithms like Word2Vec, GloVe, and those used within Transformer models (which power many LLMs) are responsible for learning these useful embeddings.

Main Benefits of Using Embeddings

Using embeddings offers several benefits for AI language processing.

First, they reduce dimensionality. Instead of dealing with a vocabulary of tens of thousands of unique words as separate items, the AI works with dense numerical vectors of a few hundred or thousand dimensions. This is more efficient for computation.

Second, they capture semantic similarity. As mentioned, words with similar meanings have similar embeddings, which is crucial for processing context and nuance in text.

Third, they allow for generalization. If the model learns something about the embedding for "dog," it can apply some of that learning to words with similar embeddings, like "puppy" or "canine," even if it has not seen them in that exact context before.

Fourth, they are contextual. Modern embeddings, especially in LLMs, can even change based on the surrounding words. The embedding for "bank" in "river bank" will be different from "bank" in "money bank." This helps resolve ambiguity and leads to better language processing by the AI.

These numerical representations are a foundational piece of technology that allows LLMs to process, interpret, and generate human-like text with impressive capability.