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A sequence model is trained to generate numerical sequences. All training examples consist of sequences that follow the simple arithmetic rule: the value at any position is twice the position number (e.g., at position 10, the value is 20). The model is only trained on sequences with a maximum length of 100 positions. After training, the model is evaluated. Which of the following evaluation results provides the strongest evidence that the model can successfully generalize its learned pattern to inputs outside the range of its training data?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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A sequence model is trained to generate numerical sequences. All training examples consist of sequences that follow the simple arithmetic rule: the value at any position is twice the position number (e.g., at position 10, the value is 20). The model is only trained on sequences with a maximum length of 100 positions. After training, the model is evaluated. Which of the following evaluation results provides the strongest evidence that the model can successfully generalize its learned pattern to inputs outside the range of its training data?
Analyzing Model Performance on Unseen Sequence Lengths
A sequence model was trained to replicate a repeating numerical pattern, but only on sequences with a maximum length of 500 steps. The following descriptions outline the model's performance under different testing conditions. Match each performance description to the most appropriate term.