Learn Before
Applying Pre-trained Models to Downstream Tasks
Once a model has undergone pre-training, various techniques are necessary to adapt it for specific downstream applications. The selection of an appropriate adaptation method is a key step in leveraging pre-trained models effectively.
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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
Selecting a Model Adaptation Strategy
Comparing Model Adaptation Techniques
A research lab has access to a single, very large, pre-trained language model with billions of parameters. Their goal is to adapt this model for over a dozen distinct, specialized scientific text analysis tasks (e.g., gene name recognition, chemical reaction classification, protein function prediction). They have limited computational resources and cannot afford to store a separate, multi-billion parameter model for each of the dozen tasks. Which of the following adaptation approaches best addresses their primary constraints?