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Analyzing Model Learning via Token Probability
An engineer is training a language model that learns by predicting masked words in sentences. They monitor the model's performance on a specific validation sentence: 'The sun rises in the [MASK].' The original word at the [MASK] position is 'east'. The table below shows the probability the model assigned to the correct word 'east' at different stages of training. Analyze the trend shown in the table and explain what it indicates about the model's learning process.
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Ch.1 Pre-training - Foundations of Large Language Models
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A masked language model is given the input sequence: 'The quick brown [MASK] jumps over the lazy dog.' The original, unmasked token at the
[MASK]position was 'fox'. Two different versions of the model, Model A and Model B, are used to predict the masked token.- Model A assigns a probability of 0.85 to the token 'fox'.
- Model B assigns a probability of 0.15 to the token 'fox', and its highest predicted probability is 0.40 for the token 'cat'.
Based on the probability assigned to the correct, original token, which of the following statements provides the most accurate analysis of the models' performance on this specific example?
Analyzing Model Learning via Token Probability
A language model is being trained on the task of filling in masked words. At an early stage of training, for the sentence 'The sun rises in the [MASK]', the model assigns a probability of 0.05 to the correct word 'east'. After many more rounds of successful training on a large dataset, the model is presented with the same masked sentence. Which of the following outcomes is the most plausible and directly reflects the objective of this training process?