Examples of Diverse Instructions and Responses in Fine-Tuning Data
Fine-tuning datasets used to teach Large Language Models instruction-following abilities contain a wide variety of tasks paired with their correct outputs. Two examples of such instruction-response pairs include:
- Mathematical Reasoning: An arithmetic instruction might ask, "If you buy 5 apples and each apple costs $1.20, how much do you spend in total?" with the expected response being "$6.00".
- Code Generation: A programming instruction might ask, "Write a Python program to calculate the sum of squares of the following numbers. 1, 2, 10, -9, 78," paired with a corresponding code response that performs the calculation:
numbers = [1, 2, 10, -9, 78] sum_of_squares = sum(x**2 for x in numbers) print(sum_of_squares)
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Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
A development team has a powerful, pre-trained language model that excels at predicting the next word in a sentence. Their goal is to adapt this model into a versatile, instruction-following assistant capable of handling a wide range of user commands. Which of the following data collections would be the most crucial and effective for this specific adaptation process?
Analyzing a Flawed Fine-Tuning Dataset
Multi-Task Learning via Instruction Fine-Tuning
Designing a Dataset for an Instruction-Following Model
Examples of Diverse Instructions and Responses in Fine-Tuning Data