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Contrastive Learning (CTL)
It assumes some observed pairs of text that are more semantically similar than randomly sampled text.
There is a score function for a text pair learned to minimize the objective function, where are a similar pair and is presumably dissimilar to .
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Data Science
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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