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Assumption of Supervised Pre-training
The underlying assumption in supervised pre-training is that different supervised learning tasks share a relationship. Because of this connection, a neural network can be initially trained on one specific task and then effectively transferred to a different task, requiring only some additional tuning or training effort to adapt to the new objective.
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Foundations of Large Language Models
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Process of Adapting a Supervised Pre-trained Model
Advantages of Supervised Pre-training
Disadvantages of Supervised Pre-training
Example of a Supervised Pre-training Task
A startup is building a system to automatically categorize legal contracts into specific sub-types (e.g., 'lease agreement', 'employment contract', 'non-disclosure agreement'). They have a very small, private dataset of 500 labeled contracts. Their proposed strategy is to first train a large neural network on a massive, publicly available dataset of millions of labeled news articles, classifying them by topic (e.g., 'sports', 'politics', 'technology'). After this initial training, they plan to adapt the model to their legal contract categorization task. What is the most significant weakness of this proposed pre-training approach for their specific goal?
A machine learning engineer wants to use a supervised pre-training approach to build a model that can detect toxic comments online. Arrange the following steps in the correct chronological order to reflect this process.
Evaluating a Pre-training Strategy for Scientific Text
Assumption of Supervised Pre-training