Modeling LLM Performance with Scaling Functions
To understand and analyze how a Large Language Model's performance scales, it is common practice to represent its performance as a mathematical function of various training factors. In its simplest application, this involves expressing the model's loss or error as a function of a single variable, such as the size of the training dataset or the number of model parameters.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Modeling LLM Performance with Scaling Functions
Guiding Role of Scaling Laws in LLM Research
Predictive Utility of Scaling Laws for LLM Training Decisions
Evolving Understanding of Scaling Laws
Insufficiency of Model Size Scaling for AGI
An AI research lab is developing a new large language model and has a fixed computational budget. According to the principles that formalize the relationship between a model's performance, its size, and the quantity of its training data, which of the following strategies is most likely to yield the best-performing model within their budget?
Evaluating Competing LLM Training Strategies
The Strategic Importance of Predictable Performance Scaling
Learn After
Absence of a Universal Scaling Law
A research team is developing a new language model. They train several versions of the model, each with a different number of parameters, while keeping the training dataset size fixed. They plot the final training loss for each model version against its parameter count. The resulting graph shows a consistent, downward-curving trend: as the number of parameters increases, the loss decreases, but the amount of improvement gets smaller with each increase. Based on this observation, what is the most accurate conclusion the team can draw?
Optimizing LLM Training Budget
Comparing Single-Variable Scaling Functions