Limitation of MLM: Ignoring Dependencies Between Masked Tokens
A key limitation of the auto-encoding objective in Masked Language Modeling (MLM) is its failure to account for dependencies among the masked tokens. The model is trained to predict each masked token independently of the others. For example, if two tokens and in a sequence are masked, the prediction for the first masked token () is generated independently of the second masked token (), even though should ideally be considered within the context of .
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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During a self-supervised pre-training process, a model is given an input sequence where one word has been replaced by a special symbol, for example: 'The quick brown [MASK] jumps over the lazy dog.'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
Diagnosing a Language Model's Predictive Behavior
A language model pre-trained with a standard masked language modeling objective is given the input sentence: 'The capital of the United Kingdom is [MASK] [MASK].' Which statement best describes how the model will predict the two masked tokens?
Consequences of Independent Predictions in Language Models
Permuted Language Modeling (PLM)