The Pre-training and Fine-tuning Paradigm
The pre-training and fine-tuning paradigm is a method motivated by the goal of creating adaptable, general-purpose systems for universal language understanding and generation. It involves separating the common components of neural network architectures, such as Transformers, and training them on vast amounts of unlabeled data using self-supervision. The resulting systems, known as foundation models, can be easily adapted for specific downstream applications via fine-tuning or prompting. This paradigm shift has enormously transformed natural language processing, meaning that in many cases, large-scale supervised learning for specific tasks is no longer required.
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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
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.1 Pre-training - Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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Scope of Introductory Discussion on Pre-training in NLP