What are embeddings in the context of LLMs?

In the context of large language models (LLMs), embeddings are numerical representations of text that capture semantic meaning. These representations transform words, phrases, or entire documents into vectors (arrays of numbers) in a high-dimensional space. By converting text into numbers, LLMs can process language mathematically, identifying patterns and relationships that are not obvious in raw text. For example, the word “dog” might be represented as a vector like [0.25, -0.7, 0.3, …], while “puppy” could be a similar vector but with slight differences. The key idea is that words with related meanings or usage contexts are positioned closer to each other in this vector space, enabling the model to generalize and reason about language.

LLMs generate embeddings through training on vast amounts of text data. During training, the model learns to adjust these vectors so that words appearing in similar contexts (e.g., “cat” and “kitten”) have similar embeddings. This process often involves neural networks, where the embedding layer is one of the first steps in processing input text. For instance, in models like GPT or BERT, each token (a word or subword unit) is mapped to an embedding vector before being passed through the model’s layers. Additionally, some models use positional embeddings to encode the order of words in a sentence, ensuring the model understands sequential relationships. These embeddings are not static; they evolve during training to better capture nuanced semantic and syntactic features.

Developers use embeddings for tasks like semantic search, text classification, or clustering. For example, in a search application, embeddings can compare the similarity between a query and documents by measuring the distance between their vectors. Tools like OpenAI’s API or libraries like Hugging Face Transformers provide pre-trained embedding models that developers can integrate directly. A simple code snippet using PyTorch might load a pre-trained BERT model, tokenize text, and extract embeddings for further analysis. Embeddings can also represent entire sentences by averaging word vectors or using specialized techniques like sentence-BERT. By leveraging these numerical representations, developers build systems that understand context, detect paraphrases, or recommend related content, all while relying on the mathematical properties of vectors to encode linguistic meaning.