In the context of Large Language Models (LLMs), parameters are the internal variables that the model learns during training. These parameters, which include weights and biases, determine how the model processes input data and generates output. The number of parameters in an LLM is a key factor influencing its capacity to understand and generate human-like text.
Understanding Parameters in LLMs:
- Weights and Biases: Weights are numerical values that adjust the strength of connections between neurons in the model’s neural network. Biases are additional parameters that allow the model to make adjustments to the output, enabling it to better fit the training data.
- Training Process: During training, the model adjusts these parameters to minimize the difference between its predictions and the actual outcomes. This process involves optimizing the parameters to capture patterns and relationships within the training data.
- Model Capacity: The number of parameters in an LLM is often associated with its capacity to learn complex patterns. Generally, a larger number of parameters can lead to better performance on a wider range of tasks. However, this also increases computational requirements and the risk of overfitting.