Consecutive Token Masking in MLM
In Masked Language Modeling, the random selection of tokens for masking can result in the masking of consecutive tokens within a sequence [Joshi et al., 2020]. This means that two or more adjacent words may be replaced by [MASK] symbols in a single training instance.
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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During a self-supervised pre-training process, a model is given an input sequence where one word has been replaced by a special symbol, for example: 'The quick brown [MASK] jumps over the lazy dog.'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
Example of Denoising Task with Consecutive Token Masking
Representing Masked Spans with Sentinel Tokens
A language model is being trained to predict masked words in a text. Consider two different masking strategies:
Strategy 1: 15% of the words in a sentence are masked individually at random positions. Example:
The quick [MASK] fox jumps [MASK] the lazy dog.Strategy 2: A contiguous span of several words is masked. Example:
The quick [MASK] [MASK] [MASK] the lazy dog.How does using Strategy 2 (masking a contiguous span) primarily alter the learning challenge for the model compared to Strategy 1?
Analyzing a Masked Language Modeling Task
Analyzing Model Performance Discrepancy
Analyzing the Challenge of Consecutive Masking