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Diagnosing a Data Generation Pipeline Issue
Based on the provided scenario, identify the most likely missing or poorly implemented step in the team's data generation cycle and explain why its failure leads to the observed problem.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Sample Generation in Self-Instruct
Filtering in Self-Instruct
Task Pool in Self-Instruct
Initialization of the Task Pool in Self-Instruct
Instruction Generation in Self-Instruct
Refining Prompt Templates in Self-Instruct
An AI development team wants to expand a small, manually-created set of instruction-following data into a much larger dataset for fine-tuning a language model. They decide to use the model itself to generate new data in an iterative loop. Which of the following procedures correctly describes the core cycle for generating one new, high-quality data point?
A team is using an iterative method to generate a large dataset for fine-tuning a language model, starting from a small set of examples. Arrange the core steps of a single cycle of this process in the correct order.
Diagnosing a Data Generation Pipeline Issue