Introduction to Text Embeddings in Large Language Models: The Fundamental Building Block

3D plot of text embeddings showing related words clustered together

In the realm of Natural Language Processing (NLP), Text Embeddings in Large Language Models have emerged as a fundamental building block. They are the secret sauce that enables machines to understand and process human language, transforming the unstructured text data into a structured form that computers can process effectively.


Image: Text Embeddings in Large Language Models

Understanding Text Embeddings: From Words to Numerical Vectors

The concept of Text Embeddings revolves around representing words or longer text passages as ordered sequences of numbers, known as vectors. This numerical representation allows computers to process and understand language. The process involves converting words into numerical vectors that neural networks can understand, thereby bridging the gap between human language and machine understanding.

The Role of One-Hot Encoding in Text Embeddings for Neural Networks

One of the initial methods used to create these numerical representations was One-Hot Encoding. This technique involves representing each word in the vocabulary as a vector, with a dimension equal to the size of the vocabulary. Each word is represented by a vector where all elements are zero, except for the one corresponding to the word, which is set to one. However, this method results in large vectors with mostly zeroes, especially with large vocabularies, leading to computational inefficiencies.

Exploring Word Embeddings for Language Models: Beyond One-Hot Encoding

To overcome the limitations of One-Hot Encoding, the concept of Word Embeddings was introduced. Word Embeddings represent words in a way that related words are close to each other in the vector space, and words with different meanings are far apart. This method captures the crucial characteristic of language where related concepts stay together. It’s a more efficient way of representing words as it reduces the dimensionality and captures the semantic relationships between words.

The Significance of Dimensions in Text Embeddings and Their Representation

In Word Embeddings, dimensions can represent various concepts. For example, in a two-dimensional embedding, one dimension could represent “age” and the other “gender”. More dimensions can represent more concepts. For instance, GPT3, one of the most advanced language models developed by OpenAI, uses 12,288 dimensions to encode their vocabulary. This high-dimensional representation allows GPT3 to capture a wide range of concepts and semantic relationships.

Implementing Word Embeddings: A Practical Approach

The process of creating Word Embeddings is done by the neural network during the training phase. It involves looking at thousands of documents and moving every word to be closest to the other words that show up in the same context on those documents. This process results in a high-dimensional vector space where the position of each word is learned based on its context.

Expanding the Horizon: Text Embeddings for Images and Their Potential

The concept of embeddings is not limited to text. It can also be applied to images. In the context of images, embeddings can be used to represent images in a way that similar images are close to each other in the vector space, and dissimilar images are far apart. This has significant applications in image recognition and classification tasks.

This article was inspired by a tweet thread by Santiago. If you found this article helpful and want to dive deeper into the world of Text Embeddings and Large Language Model

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