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BERT's Masked Language Model Pre-training Process
BERT's Masked Language Model (MLM) is trained using a specific data corruption process. First, 15% of the tokens in an input sequence are randomly selected as prediction targets. Then, these selected tokens are modified according to a fixed distribution: 80% are replaced with a special [MASK] token, 10% are replaced with a random token from the vocabulary, and the remaining 10% are left unchanged. This strategy creates a 'noisy' version of the input. The Transformer encoder processes this corrupted sequence, and the model's objective is to predict the original, unmodified tokens based on the output hidden states of the selected positions.
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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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Example of a Two-Sentence Input for BERT
BERT's Masked Language Model Pre-training Process
A language model is trained on a large corpus of text. During this training, it is frequently presented with sentences where a single word has been hidden, such as: 'The scientist carefully examined the sample under the [HIDDEN]'. The model's sole objective is to predict the original, hidden word. What is the most significant advantage of this training objective for the model's understanding of language?
Bidirectional Context in Language Modeling
Analysis of a Language Model Training Objective
Selecting a Pre-training Objective Mix for a Corporate LLM
Diagnosing Pre-training Objective Mismatch from Product Failures
Choosing a Pre-training Objective Under Data Constraints and Deployment Needs
Selecting a Pre-training Objective for a Regulated Enterprise Assistant
Root-Cause Analysis of Pre-training Objective Leakage and Coherence Failures
Pre-training Objective Choice for a Multi-Modal Enterprise Writing Assistant
Your team is pre-training an internal LLM for a co...
Your team is building an internal model that must ...
Your team is pre-training a text model for an inte...
Your team is pre-training an internal LLM to suppo...
Transitioning from Masked Language Modeling to Downstream Tasks
Embedding of the MASK Symbol
Generalization of Masked Language Modeling to Autoregressive Modeling
Example of Simulating Standard Language Modeling via Masking
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Illustrative Example of BERT's MLM Pre-training Pipeline
During a specific language model pre-training procedure, 15% of tokens in an input sequence are chosen for prediction. Of these chosen tokens, 80% are replaced by a special
[MASK]symbol, 10% are replaced by a random token from the vocabulary, and 10% remain unchanged. What is the primary analytical reason for including the steps where tokens are replaced by a random one or left unchanged, instead of simply replacing all 100% of the chosen tokens with the[MASK]symbol?Calculating Token Modifications in Pre-training
A specific pre-training process for language models involves intentionally corrupting an input sequence and then training the model to reconstruct the original. Arrange the following steps of this data corruption and training objective in the correct chronological order.