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Types of LLM Scaling
The scaling of Large Language Models, aimed at enhancing performance and computational power, can be classified according to the developmental stage where it is applied. The main categories are pre-training scaling, fine-tuning scaling, and inference-time scaling.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Architectural Adaptation of LLMs for Long Sequences
Types of LLM Scaling
Multifaceted Nature of LLM Scaling
Inference-Time Compute Scaling for Improved Reasoning
A research lab has a powerful language model that is highly effective at generating short, creative story paragraphs. The lab now wants to use this model to write entire multi-chapter novels, which requires maintaining plot consistency and character arcs over tens of thousands of words. Which of the following development priorities best represents a shift in scaling dimension to meet this new requirement?
Evaluating a Model Scaling Strategy
Scaling LLMs Beyond Size
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Comparison of Scaling Approaches: Parameter Updates vs. Inference-Time Methods
A development team is tasked with improving a large language model's performance for a specific enterprise function. They decide to continue the training process on a new, curated dataset composed of 10,000 internal company documents. This additional training adjusts the model's existing parameters to better suit the company's specific terminology and tasks. Based on the stage of the model's lifecycle where this improvement is applied, which type of scaling is being implemented?
Match each scenario describing an enhancement to a large language model with the corresponding type of scaling being applied.
Strategic LLM Enhancement for a Resource-Constrained Startup