Performance Degradation and Early Stopping in Pre-training
During the pre-training of language models, performance can begin to decline after a certain point. This degradation is sometimes attributed to interference, where learning new information negatively impacts previously learned knowledge. To counteract this, a practical strategy is to implement early stopping, which involves halting the training process to prevent such interference and preserve the model's optimal performance.
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Ch.2 Generative Models - 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During a self-supervised pre-training process, a model is given an input sequence where one word has been replaced by a special symbol, for example: 'The quick brown [MASK] jumps over the lazy dog.'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
A machine learning engineer is pre-training a large language model. They monitor the model's performance on a separate, unseen dataset after every 10,000 training steps. They observe the following trend:
- Steps 1-100,000: Performance steadily improves.
- Step 110,000: The model achieves its best performance so far.
- Steps 120,000-150,000: Performance consistently worsens with each measurement.
Based on this observation, what is the most appropriate immediate action to ensure the best possible model is obtained from this training run?
Analyzing a Language Model's Pre-training Log
Rationale for Early Stopping in Model Pre-training