Which hyperparameter is NOT typically adjusted when tuning LLMs?

Question

BlackTom AI · Accepted Answer

Question restatement: The prompt asks which hyperparameter is NOT typically adjusted when tuning large language models (LLMs).

Option A: Model size. In practice, researchers and engineers often vary model size (number of parameters, layers, hidden dimensions) as a fundamental part of scaling experiments. While not a nightly tweak, it is a primary lever in model tuning and capacity planning, making this option something that is commonly adjusted in many tuning pipelines.

Option B: Learning rate. The learning rate is a core hyperparameter in training neural networks, including LLMs. It is routinely tuned (often via schedules, warmups, and schedule selection) to ensure stable convergence and good performance, so this is clearly a parameter that is adjusted.

Option C: Number of neurons in convolutional layers. Large language models used in most current practice are transformer-based and do not employ convolutional layers as a core architectural component. Since convolutional layers are not typically part of standard LLM architectures, the specific count of neurons in such layers is not a relevant or adjustable hyperparameter for tuning LLMs. This option describes a parameter that either does not exist in the common LLM design or is not a tuning knob used in practice, making it the one that is not typically adjusted.

Option D: [Not provided in the answer options content; only three options are present: Model size, Learning rate, Number of neurons in convolutional layers.] If we consider the list as given, there is no fourth distinct hyperparameter to evaluate here.

Additional clarification: The central misconception to avoid is thinking all neural network components offer tunable knobs for LLM tuning. In practice, while many hyperparameters (like model size and learning rate) are routinely tuned, architectural components that are not part of the model's design (such as convolutional layers in standard LLMs) do not provide meaningful tuning knobs during typical LLM fine-tuning or training workflows. This distinguishes the non-applicable architectural parameter from the standard, adjustable hyperparameters.

Learning AI Through Visualization 4 Module 5 Quiz

Which hyperparameter is NOT typically adjusted when tuning LLMs?

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