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In each iteration of the Self-Instruct process, a small subset of instructions is selected from the task pool to be used as prompts for generating new instructions. To maintain diversity in the generated tasks, this selection can include a mix of both the initial, human-written seed instructions and the instructions previously generated by the Large Language Model.

Instruction Sampling for Diversity in Self-Instruct

Based on the methodology described in the case study, what is the most likely flaw in the team's selection strategy that is causing the observed decrease in diversity, and why is this flaw detrimental?

Diagnosing Dataset Generation Issues

In a self-instruction process for generating new tasks, a common strategy is to sample from a pool containing both the original, human-created seed instructions and the instructions previously generated by the model. Explain the primary reason for including *both* types of instructions in the sampling pool, rather than relying on just one type.

Rationale for Mixed Instruction Sampling

A research team is using a self-instruction method to generate a large dataset of tasks. In their process, for each new generation step, they exclusively sample from the small, initial set of human-written examples to prompt the language model. What is the most probable outcome for the final dataset if they follow this strategy?

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