When people talk about Large Language Models (LLMs) like GPT or BERT, you'll often hear phrases like "This model has 70 billion parameters" or "That model only has 3 million parameters." If you’re new to this field, this can sound confusing. Don’t worry—this guide will break it down into simple terms and examples!
What Are Parameters in LLMs?
Parameters in an LLM are numerical values that the model learns during training to understand language. Think of parameters as the “knobs” or “settings” the model adjusts to make better predictions, such as guessing the next word in a sentence or answering a question.
Imagine a network of interconnected neurons. Each connection has a strength (the weight). By adjusting these weights, the network learns to recognize patterns in the data and make accurate predictions. These weights are learned by the model during training by adjusting them to minimize the error between the predicted output and the actual output.
In a neural network, every connection between nodes has a weight.
For example, if a neuron 'A' connects to a neuron 'B', the weight 'w' determines how much the output of A contributes to the input of B. Weights determine the importance of inputs. Larger weights give more importance to certain inputs, while smaller weights give less.
Let's take a real-world analogy to understand it better. Suppose you’re baking a cake, and the recipe calls for flour, sugar, and eggs. The amounts of these ingredients (e.g., 2 cups of flour, 1 cup of sugar) are like parameters—they determine how the final cake turns out. If you tweak the amounts (parameters), you can make the cake sweeter, fluffier, or drier.
Similarly, in LLM, Parameters are the "ingredients" that determine how the model processes language. By adjusting these parameters during training, the model learns to “understand” words, grammar, context, and even complex ideas.
Example of weights in neural Network
Let’s take a simple neural network with 2 input nodes, 1 hidden layer with 2 neurons, and 1 output node.
Input Layer
Input Values x1, x2
Each input is connected to the hidden layer neurons with weights w(1, 1), w(1, 2), w(2, 1) and w(2, 2).
Hidden Layer
Each neuron in the hidden layer combines the weighted inputs
z1 = x1 * w(1, 1) + x2 * w(2, 1) + b1
z2 = x1 * w(1, 2) + x2 * w(2, 2) + b2
Here b1, b2 are biases, which are also model parameters. In neural networks, biases are parameters added to neurons to help the model make accurate predictions. Unlike weights, which scale the input, biases shift the output of a neuron, allowing the model to learn patterns that don’t pass through the origin (zero point).
Imagine adjusting a volume knob (weight) to control sound. Without bias, the volume always starts at 0. Adding bias is like setting a minimum volume level (e.g., 3) even when the knob is at the lowest position. This allows finer control.
Output Layer
The hidden layer outputs are passed to the output layer, again with weights.
y = z1 * w(3, 1) + z2 * w(3, 2) + b3
Key Characteristics of Model Parameters
· Learned automatically by the model.
· Change dynamically as the model trains on data.
· Example: A model adjusts weights and biases to minimize errors during training.
What Happens During Training?
When an LLM is trained,
· The model starts with random parameter values.
· It processes millions of examples (e.g., text from books, websites, etc.).
· It adjusts the parameters repeatedly to minimize errors (e.g., guessing the wrong word).
The final model has billions of optimized parameters that allow it to:
· Understand language.
· Generate human-like text.
· Perform various tasks (e.g., answering questions, summarizing text).
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