Visualizing Empirical Scaling Laws for LLM Loss
The empirical power-law relationships governing a language model's performance can be graphically represented by plotting the test loss against the model size () and the training dataset size (). These visualizations illustrate how the test loss predictably decreases as a function of , defined mathematically as , and as a function of , defined as .
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Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Combined Power Law for LLM Loss with Model and Dataset Size
A research team is deciding between two language model sizes. Model A will have 10 billion parameters, and Model B will have 100 billion parameters. According to the empirical relationship where performance loss (L) is a function of the number of parameters (N), as shown in the formula below, which model should the team choose to achieve a lower final loss, and what is the justification?
Interpreting Model Scaling Effects
Interpreting the Model Scaling Formula
Visualizing Empirical Scaling Laws for LLM Loss
Power Law Fit for Test Loss vs. Model and Dataset Size