An AI development team is debating how to improve their language model's performance on tasks that require understanding long conversations. Option 1 is to increase the size of the system that stores contextual information from the conversation. Option 2 is to increase the number of trainable parameters in the model's architecture. Which statement best evaluates the fundamental difference between these two options?

Analyzing Model Enhancements

A language model's complexity, measured by its total number of trainable parameters, is directly proportional to its memory capacity, which is the amount of storage allocated for contextual information.

Parameter Count as a Measure of Model Complexity