A language model is being trained on a large dataset of text sequences. After a single parameter update, the model's calculated log-probability for one specific sequence in the dataset increases by 2.5, while the log-probabilities for all other sequences in the dataset remain exactly the same. How does this change affect the overall maximum likelihood training objective for the entire dataset?
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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
Ch.2 Generative Models - Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Application in Bloom's Taxonomy
Cognitive Psychology
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A language model is being trained on a dataset containing a mix of very short sequences and a few extremely long sequences. A developer observes that the overall training objective, which is the sum of the log-probabilities of all sequences in the dataset, seems to be disproportionately influenced by the model's performance on the few long sequences. Which of the following best explains this observation?
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A language model is being trained on a large dataset of text sequences. After a single parameter update, the model's calculated log-probability for one specific sequence in the dataset increases by 2.5, while the log-probabilities for all other sequences in the dataset remain exactly the same. How does this change affect the overall maximum likelihood training objective for the entire dataset?
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