Drawback of Masked Language Modeling: The [MASK] Token Discrepancy
A significant drawback of Masked Language Modeling is its reliance on a special token during the training phase. Because this artificial token is not present in natural text during testing or real-world inference, it creates a discrepancy between how the model is trained and how it operates in practice at test time.
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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
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Comparison of Masked vs. Causal Language Modeling
Formal Definition of the Masking Process in MLM
Example of Masked Language Modeling with Single and Multiple Masks
Training Objective of Masked Language Modeling (MLM)
Drawback of Masked Language Modeling: The [MASK] Token Discrepancy
Limitation of MLM: Ignoring Dependencies Between Masked Tokens
The Generator in Replaced Token Detection
Consecutive Token Masking in MLM
Token Selection and Modification Strategy in BERT's MLM
BERT's Masked Language Modeling Pre-training Pipeline
Performance Degradation and Early Stopping in Pre-training
Flexibility of Masked Language Modeling for Encoder-Decoder Training
Training Objective of the Standard BERT Model
During a self-supervised pre-training process, a model is given an input sequence where one word has been replaced by a special symbol, for example: 'The quick brown [MASK] jumps over the lazy dog.' The model's objective is to predict the original word, 'fox'. Which of the following is the direct input used by the final output layer to make this specific prediction?
Original Sequence for Masking and Deletion Examples
Arrange the following steps in the correct order to describe the process of pre-training an encoder model using a masked language modeling objective.
Evaluating a Pre-training Strategy for a Specific Application
Learn After
Impact of Pre-training/Fine-tuning Mismatch on Downstream Tasks
A language model is first trained on a large text corpus where some words in each sentence are replaced with a special
[MASK]symbol, and the model's goal is to predict the original words. Subsequently, this pre-trained model is adapted for a specific task, such as sentiment analysis, using a new dataset of complete, un-masked sentences. Which of the following statements best analyzes the primary architectural conflict that arises between these two phases?Troubleshooting a Pre-trained Model's Performance
Permuted Language Modeling (PLM)