Learn Before
Supervised Pre-training
Supervised pre-training is an approach where a neural network, such as a sequence model designed to encode inputs into representations, is initially trained on a supervised task. This is achieved by combining the core model with a classification layer to form a complete system. This system is then trained on a labeled dataset for a specific pre-training objective, like sentiment classification, before it is adapted for other downstream tasks.
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References
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
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Contrastive Learning (CTL)
Extensions of PTMs
Applying and Adapting Pre-trained Models to Downstream Tasks
Unsupervised Pre-training
Supervised Pre-training
Self-Supervised Learning
Comparison of Pre-training Paradigms
Rationale for Categorizing Pre-training Tasks by Objective
Denoising Autoencoding
Comparability of Pre-training Tasks
Generality of Pre-training Tasks and Performance
Applying Pre-trained Models to Downstream Tasks
Identifying a Pre-training Strategy
Breadth of Pre-training Tasks
A research team is developing a new language model and is considering different pre-training approaches. Match each pre-training scenario below with the correct category of learning it represents.
A language model is being trained on a large corpus of text from the internet. The training process involves randomly hiding 15% of the words in each sentence and then tasking the model with predicting the original identity of these hidden words based on the surrounding context. Which category of pre-training task does this scenario best exemplify, and why?
Comparing Pre-training Task Categories
Comparison of Pre-training Tasks
Learn After
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