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Refining Prompt Templates in Self-Instruct
The prompt templates used within the Self-Instruct framework for generating new instructions and fine-tuning samples are not necessarily fixed. While simple templates can serve as a starting point, they can be refined and made more sophisticated to yield instructions and samples that are more diverse and accurate, thereby improving the quality of the final fine-tuning dataset.
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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
Related
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
Learn After
Diagnosing Low Diversity in a Generated Dataset
Consequences of Static Prompt Structures in Automated Data Generation
Biased Predictions in LLM-based Synthetic Data Generation
An AI development team is using a large language model to automatically generate a dataset of programming problems and their solutions. They start with a simple instruction-generation prompt like:
Generate a new programming problem.After generating 10,000 examples, they find that the problems are repetitive (e.g., mostly sorting lists) and the generated solutions are often suboptimal. Which of the following modifications to their process would be the most effective first step to improve both the diversity of the problems and the quality of the solutions?