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Data Demand for Large Language Models
As neural networks are scaled up, their demand for data increases significantly. Developing Large Language Models requires pre-training on massive datasets, often containing trillions of tokens, which is orders of magnitude larger than the data used to train conventional Natural Language Processing models.
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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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Key Issues in Large-Scale LLM Training
A research lab is pre-training a new language model with billions of parameters on a petabyte-scale dataset. Midway through the process, they observe that the model's learning progress becomes highly erratic, and the training process frequently crashes. Which statement best analyzes the fundamental challenge they are facing?
Model Modification for Large-Scale LLM Training
Distributed Training for Large-Scale LLMs
Scaling Laws for LLMs
During the pre-training phase of a large language model, consistently increasing the volume of the training data and the number of model parameters will reliably lead to a more stable training process and better performance.
LLM Pre-training Strategy Analysis
Data Demand for Large Language Models