Evaluating an MLM Training Implementation
A junior data scientist is implementing a masked language model from scratch. During a code review, a senior colleague observes that the loss function is only being calculated based on the model's predictions for the tokens that were masked in the input. The junior data scientist is concerned this is an error and that the loss should be calculated over the entire sequence to ensure the model learns to reconstruct the full original sentence. As the senior colleague, how would you respond? Explain whether the current implementation is correct or incorrect, and justify your reasoning based on the practical training objective of masked language modeling.
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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
Application in Bloom's Taxonomy
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MLM Training Objective using Cross-Entropy Loss
MLM Training Objective as Maximum Likelihood Estimation
A language model is being trained using a masked language modeling objective. The input is a sentence where some words have been replaced with a
[MASK]token. While the high-level goal is to enable the model to reconstruct the original sentence from this corrupted input, the practical training objective is more specific. Which statement best analyzes the actual, simplified objective the model optimizes during training and the reason for this simplification?Evaluating an MLM Training Implementation
During the training of a language model with a masked language modeling objective, the model is optimized to predict the entire original text sequence, including the tokens that were not masked, from the corrupted input.