Standard Optimization Objective for Transformer Language Models
The training of Transformer-based language models is generally formulated as a standard neural network optimization task. The goal is to find the optimal model parameters by maximizing a likelihood-based objective function over a dataset , mathematically expressed as . This optimization process is typically implemented using gradient descent algorithms, which are well-supported by standard deep learning toolkits.
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References
Speech and Language Processing (3rd ed. draft)
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Data Science
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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Standard Optimization Objective for Transformer Language Models
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