Token Embeddings in LLM's

Edition #118 | 31th Mar 2025

Hello !!
Welcome to this edition of Business Analytics Review!

Today, we’re diving into the mechanics behind large language models (LLMs) like ChatGPT and Gemini. Specifically, we’ll explore token embeddings—the unsung heroes that enable machines to understand and generate human-like text. Let’s break down this concept in a way that’s both technical and approachable.

What Are Token Embeddings?

Token embeddings are numerical representations of words or subwords (tokens) that capture their semantic and syntactic meaning. When you type a query into an LLM, the text is first split into tokens (words or parts of words). Each token is then converted into a high-dimensional vector (a list of numbers) through a process called embedding. These vectors allow the model to process language mathematically, identifying relationships like synonyms, analogies, or contextual nuances.

For example, the word “bank” might be represented as a vector close to “river” in one context and “finance” in another. This flexibility is key to LLMs’ ability to handle polysemy (words with multiple meanings).

How Do Token Embeddings Work in LLMs?

1. Tokenization:
   Text is split into tokens using methods like Byte-Pair Encoding (BPE). For instance, “unhappy” might become “un” + “happy”.

2. Embedding Layer:
   Each token is mapped to a dense vector (e.g., 768 dimensions in smaller models, 12,288 in GPT-4). These vectors are learned during training to capture semantic relationships.

3. Contextualization:
   In transformer-based models, positional embeddings and attention mechanisms refine these vectors to reflect context. For example, “bat” in “baseball bat” vs. “flying bat” will have different embeddings.

A recent breakthrough (as highlighted in research like A Text is Worth Several Tokens) shows that LLM embeddings align closely with key tokens in the input, enabling efficient retrieval and generation. Adjusting the first principal component of these embeddings can enhance tasks like semantic search.

Types of Embeddings

• Traditional Word Embeddings: Static vectors (e.g., Word2Vec) that don’t adapt to context.

• Contextual Embeddings: Dynamic vectors (e.g., BERT) that change based on surrounding text.

• Positional Embeddings: Encode token positions to maintain word order.

Why Token Embeddings Matter

• Efficiency: Compress language into a form machines can process.

• Semantic Richness: Capture nuances like irony, sarcasm, or domain-specific jargon.

• Multi-Modal Potential: Frameworks like TEAL (Tokenize and Embed All) extend embeddings to images, audio, and more, enabling unified processing across modalities.

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Further Reading

1. Foundations of LLMs: Tokenization and Embeddings
   DZone Article
   A primer on how tokenization and embeddings power modern LLMs.

2. TEAL: Tokenize and Embed All for Multi-Modal LLMs
   arXiv Paper
   Explores unifying text, image, and audio into a shared embedding space.

3. Text Embeddings Secretly Align with Key Tokens
   Research Paper
   Discovers how adjusting embeddings improves retrieval efficiency by 80% with sparse methods.

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Recommended Tool: Hugging Face Transformers

Website: huggingface.co
Hugging Face’s Transformers library provides pre-trained models (like BERT and GPT) and tools to experiment with token embeddings. Its SentenceTransformers module simplifies generating context-aware embeddings for tasks like semantic search and clustering.

Final Thoughts

Token embeddings are the backbone of LLMs, transforming messy human language into structured numerical data. As frameworks like TEAL push multi-modal boundaries, the future of AI lies in unifying how we represent text, images, and sound—ushering in smarter, more intuitive systems.

Until next time, keep embedding curiosity into your learning journey!