3.2 Overview of Training Steps

Training Large Language Models (LLMs) is a resource-intensive process that consumes substantial computational power and time. However, the result is an advanced model capable of sophisticated language understanding and generation. The training progresses through a series of steps, each significantly impacting the model's learning capability. This section explains the fundamental processes involved in LLM training.

In the previous section, "Datasets and Preprocessing", we discussed the importance of data preparation and tokenization for LLM training. Here, we examine how the model learns through specific training steps.

1. Initialization

The training process starts with parameter initialization. LLMs contain millions to billions of parameters, which are initially set to random values. At this stage, the model has not learned anything, and its text prediction accuracy is very low. Initialization serves as the starting point for the model to adjust its parameters throughout the learning process.

2. Forward Propagation

The next step in the training process is forward propagation. Input data (text) is fed into the model, and predictions are generated. During this process, the data passes through multiple layers, resulting in the final output. For example, in a text generation task, the model predicts the next word in a given sentence.

3. Loss Calculation

After generating predictions, the model's error is calculated using a loss function. The loss indicates the difference between the model's predictions and the actual target data. A higher loss value means the predictions are less accurate. In LLM training, the cross-entropy loss function is commonly used to evaluate how well the model’s predictions align with the correct answers.

4. Backward Propagation

Next, the model’s parameters are updated through backward propagation. Based on the loss value, gradient descent is applied to adjust the model's parameters. This process allows the model to gradually improve its predictions. Backward propagation is central to the learning process, optimizing the model for accurate predictions.

5. Epoch Repetition

These processes are repeated in units called epochs. An epoch represents one complete pass through the entire training dataset. LLM training typically requires multiple epochs. With each epoch, the parameters are fine-tuned, and the model’s prediction accuracy improves. However, increasing the number of epochs too much can lead to overfitting, where the model performs well on training data but poorly on unseen data, so a balanced approach is necessary.

Learning Rate and Hyperparameter Tuning

One of the key factors during training is the learning rate. The learning rate determines the extent to which the model’s parameters are adjusted. A high learning rate can cause the model to oscillate without converging, while a low learning rate may slow down the learning process. Setting an optimal learning rate is critical for successful training. Additionally, other hyperparameters like batch size and dropout rate must be tuned for optimal performance.

LLM training involves multiple iterations of these steps. To optimize the training, careful selection of the loss function, tuning of the learning rate, and adjustment of hyperparameters play a significant role. These adjustments help the model achieve the best possible prediction accuracy.

In the next section, "Fine-Tuning and Transfer Learning", we will discuss methods for adapting pre-trained models to specific tasks. Learn how techniques like fine-tuning and transfer learning are used to efficiently enhance model accuracy.