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Evaluating a Training Strategy for a New LLM
As a machine learning consultant, evaluate the CTO's argument. Is tripling the data budget the most effective next step? Justify your recommendation by explaining the likely reason for the observed slow improvement.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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A research team is training a language model. They meticulously track its performance on a fixed test set as they incrementally add more training data. They observe that doubling the dataset size from 5 billion to 10 billion tokens resulted in only a very small decrease in the model's test error. Based on this observation, which of the following is the most sound judgment of their plan to immediately acquire another 90 billion tokens of data?
A research team is training a new language model and records the following test error rates as they increase the size of the training dataset:
- 1 billion tokens: Error 3.50
- 2 billion tokens: Error 3.45
- 4 billion tokens: Error 3.42
- 8 billion tokens: Error 3.10
- 16 billion tokens: Error 2.50
Based on this data, at what point does the model most clearly transition out of the initial, slow improvement stage of training?
Evaluating a Training Strategy for a New LLM