Understanding Word Embeddings, word2vec, and GPT Models: A Deep Dive into Creating Meaningful Representations for Language

In the evolving field of machine learning, words have always been at the heart of our quest to teach machines how humans communicate. From early attempts with bag-of-words models to more sophisticated neural network-based approaches, converting words into meaningful numbers has been a cornerstone of NLP (Natural Language Processing). Today, we’ll take a fascinating journey into word embeddings, word2vec, and how models like GPT evolve to enhance this revolution even further.

Words to Numbers: Why It Matters

Words are incredible—they form the bedrock of how we express emotions, ideas, and convey meaning. But for machines, words don’t inherently make sense. To a computer, words are simply strings of characters. Simply plugging words like “StatQuest” or “awesome” into a machine learning algorithm isn’t possible because these systems only understand numerical computations.

The real challenge lies in turning words into numbers in a way that captures their meanings and relationships. Imagine two words, "awesome" and "great," which are conceptually similar. If each word were represented as a random number (e.g., -7.2 for "awesome," and 134.7 for "great"), their numerical representations would not reflect their underlying semantic similarity, making it difficult for machine learning algorithms to process language effectively.

This is where word embeddings step in, allowing us to map words into numerical vectors such that relationships between words are also reflected in their numerical proximity. Simply put, if two words are similar in meaning or usage, their corresponding embeddings will also be similar in the multi-dimensional space.

But how do we create such embeddings? Enter word2vec.

Word2vec: A Simple Neural Network That Works Wonders

The Motivation

Imagine a scenario where you come across two sentences:

• "Troll 2 is great!"
• "Gymkata is great!"

While these sentences talk about two different entities ("Troll 2" and "Gymkata"), the context in which they appear is strikingly similar. A human can quickly notice this and infer that "Troll 2" and "Gymkata" might be similar in some way—both could be, let’s say, infamously bad movies. The challenge lies in making a machine recognize that their contexts should also make their numerical representations similar.

How Word2vec Works

At its core, word2vec is an ingenious neural network model designed to learn word embeddings. Here’s how it works, step by step:

1. Inputs and Outputs:

• During training, word2vec identifies a target word (e.g., "is") and predicts context words (e.g., "Troll 2" and "great") or vice versa.
• Based on the task, it uses one of two strategies:
• Continuous Bag-of-Words (CBOW): Predicts a word in the middle of a sentence using surrounding words as input features.
• Skip-gram: Predicts surrounding words using the middle word as input.

2. Structure of the Neural Network:

• The neural network looks deceptively simple. The input consists of a one-hot representation for words (essentially a vector with a "1" for the target word and 0s elsewhere).
• These inputs are passed through hidden layers with weights that the model learns during training. These weights are ultimately the word embeddings.

3. Training Process:

• Using a combination of backpropagation, the softmax function, and cross entropy loss, the model adjusts its weights so that similar words are pushed closer together in embedding space while dissimilar ones are spaced farther apart.
• For example, during training, the model might observe that both "Troll 2" and "Gymkata" often appear in the same context. It updates their embeddings to make them numerically similar.

4. Negative Sampling:

• Given the massive vocabulary that word2vec handles, updating embeddings for all words simultaneously is computationally expensive. Negative sampling helps by only updating embeddings for a subset of “negative” words (those irrelevant to the current context).

Result: Meaningful Representations

By the time training is complete, words with similar meanings or that appear in similar contexts are embedded close together in a high-dimensional vector space. This mapping captures surprisingly rich relationships:

• Arithmetic with word embeddings: vec("king") - vec("man") + vec("woman") ≈ vec("queen")

Voila! Machines can now process words in an intuitive way, making downstream NLP tasks significantly easier.

Moving Beyond Word2vec: The World of Contextual Models

While word2vec works brilliantly in capturing relationships, it is limited by the fixed embeddings it assigns to words. Words like "great" can have entirely different meanings depending on context (e.g., a compliment in “StatQuest is great!” vs. sarcasm in “Oh, great, my phone is broken.”). Word2vec doesn’t distinguish between these usages.

To address this limitation, researchers began exploring contextual embeddings, leading to the development of transformer-based architectures, including GPT (Generative Pre-trained Transformer).

What Makes GPT Special?

The Birth of Transformers

The transformer architecture introduced by Vaswani et al. in 2017 revolutionized NLP. Unlike traditional RNNs (recurrent neural networks), which processed sequences one step at a time, transformers use self-attention mechanisms to capture relationships between all words in a sequence simultaneously.

Enter GPT

OpenAI’s GPT (Generative Pre-trained Transformer) builds on the transformer architecture with a focus on generating coherent text. GPT models are unidirectional, meaning they predict the next word in a sequence from left to right. Let’s explore how they’ve evolved through versions.

GPT Evolution: From Version 1 to 2

GPT-1: A Modest Beginning

• Parameters: 110 million
• Training: Pre-trained on huge datasets using next-token prediction and fine-tuned for specific NLP tasks like classification.
• Architecture: 12-layer transformer decoder.

While GPT-1 demonstrated the power of transformers, it was relatively small in scale.

GPT-2: Scaling Up and Cutting Fine-Tuning

• Parameters: 1.5 billion—a 10x increase from GPT-1.
• Key Innovation: Zero-shot learning. Unlike GPT-1, which relied on fine-tuning for specific tasks, GPT-2 introduces a novel way to provide both task description and input directly during inference. For instance:
• Input to GPT-2: “Translate English to French: How are you?”
• Output: “Comment ça va?”
• Architecture Updates:
• Larger vocabulary size
• Higher maximum context length (from 512 tokens in GPT-1 to 1024)
• Improved handling of context for tasks such as translation, text summarization, and question answering.

GPT-2 was trained on 45 GB of high-quality web content, ensuring diversity and relevance.

Results: Contextual Brilliance

GPT-2 demonstrated that with sufficient scale, pre-trained models could perform extraordinarily well across many tasks without fine-tuning. It performed admirably in solving problems like reading comprehension and question answering—even rivaling specialized systems in some domains. However, it wasn’t perfect. For instance, tasks requiring global sentence order (like heavily shuffled paragraphs) posed a challenge.

The Power of Scale: What GPT-2 Taught Us

One of GPT-2’s key insights is the scaling law: as models grow larger, their performance improves. However, size alone isn’t enough. Efficiency matters, and techniques like negative sampling for large vocabularies help reduce computational overhead.

The Road Ahead: Toward GPT-3 and Beyond

GPT-3, with 175 billion parameters, pushes these boundaries further, exploring few-shot and one-shot learning while retaining the brilliance of GPT-2’s zero-shot abilities. Future transformer architectures promise even broader applications, including creative writing, large-scale dialogue generation, and out-of-the-box solutions across disciplines.

Final Thoughts: Scaling Connections Between Words

From word2vec’s dense embeddings to GPT’s context-aware predictions, the journey of turning words into meaningful numbers has reshaped Natural Language Processing entirely. The advancements—empowered by larger datasets, improved architectures, and innovative training techniques—have unlocked possibilities we could only dream of a decade ago.

Whether you’re crafting search algorithms, building recommendation systems, or simply marveling at the elegance of machine-generated text, it all begins with the simple act of assigning words numbers that make sense.

So remember: always be curious, and quest on! 🚀