Understanding AI Training: The Basics of Language Model Learning

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have become increasingly prominent. But how do these models actually learn? Let's dive into the fundamentals of AI training, focusing on the transformer architecture and the gradient descent optimization technique.

The Core Principle: Data Correlations

At its heart, the training process for LLMs is based on a fundamental principle from statistics: data correlations. In simpler terms, it's about understanding how certain inputs lead to specific outcomes. For language models, this translates to predicting what word comes next in a sequence of text.

A Simple Example: Training Step by Step

Let's walk through a basic example to illustrate this process:

1. Training Data: Imagine we provide the following sentence during training: "The cat was laying on the mat."

2. Input Processing: The model receives the input "the" and needs to predict the next word.

3. Prediction: If the model outputs "weather" (as in "the weather"), that would be incorrect. We're expecting "cat" based on our training sentence.

4. Optimization: This is where gradient descent comes into play. The technique adjusts the model's internal parameters (weights and biases) to:

   • Favor "cat" after "the"

   • Disfavor "weather" as it wasn't the correct choice

5. Iteration: This process repeats for each word in the sentence and across millions of other examples in the training data.

A Closer Look at the Training Process

To better understand how the model learns over time, let's examine a more detailed example of the training process:

Training sample: "The cat was lying on the mat."
Initial Distribution: " cat" 0.23, " weather" 0.25

# Initial training 
Input: "The"
Output: "The weather"
Distribution: " cat" 0.25 (+0.02), " weather" 0.23 (-0.02)

# Second iteration
Input: "The"
Output: "The cat"
Distribution: " cat" 0.27 (+0.02), " weather" 0.21 (-0.02)

. . .

# End of training
Input: "The"
Output: "The cat"
Distribution: " cat" 0.45 (+0.02), " weather" 0.12 (-0.02)

This example illustrates several key points about the training process:

1. Initial Bias: At the start, the model might have a slight preference for " weather" following "The", possibly due to more frequent occurrences in other parts of the training data.

2. Gradual Learning: With each training iteration, the model adjusts its internal probabilities. The likelihood of " cat" following "The" increases, while the probability of " weather" decreases.

3. Convergence: After multiple iterations, the model learns to strongly favor " cat" over " weather" in this specific context.

4. Correlation, Not Understanding: It's crucial to note that this process is based entirely on statistical correlations in the training data. The model doesn't truly understand the concept of a cat or weather; it's simply learning which word is more likely to follow "The" in this particular sentence.

5. Generalization: While this example focuses on a single sentence, in practice, the model would be learning from millions of examples simultaneously, allowing it to generalize across a wide range of contexts.

The Principle of Correlation

This detailed look at the training process underscores a fundamental aspect of how current AI models learn: they rely on correlation, not causation or true understanding. The model adjusts its probabilities based on patterns in the training data, but it doesn't develop a conceptual understanding of cats, weather, or the relationships between words.

This correlation-based approach is incredibly powerful and allows AI models to perform a wide range of language tasks. However, it's also the source of many of the limitations and quirks we observe in AI behavior. The model can produce highly convincing text by predicting likely word sequences, but it lacks the deeper understanding and reasoning capabilities that humans possess.

The Attention Mechanism: Enhancing Correlations

The transformer architecture introduces an additional layer of sophistication through the attention mechanism. This allows the model to focus on specific words more than others when making predictions.

In our example "The cat is laying on the mat," the attention mechanism would help the model make stronger connections between related words like "cat," "laying," and "mat."

Conclusion: Beyond Word Prediction

While this process might seem simplistic, it's the foundation upon which incredibly powerful language models are built. By training on vast amounts of text data, these models can learn complex patterns and generate human-like text on a wide range of topics.

However, it's crucial to remember that at its core, this approach is based on statistical correlations between words and phrases, not on true understanding or reasoning. This limitation is why some researchers refer to LLMs as "stochastic parrots," highlighting their statistical nature.

Understanding the basics of how AI models learn is essential for both developers and users. It helps us appreciate the capabilities of these systems while also recognising their inherent limitations.