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Components of the Input in an Instruction Fine-Tuning Sample
The input portion of an instruction fine-tuning sample is composed of several elements. These typically include the specific instructions for the task, system information (also known as a system prefix), and any other relevant information provided by the user.
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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
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Components of the Input in an Instruction Fine-Tuning Sample
A machine learning engineer is preparing a dataset to train a language model to summarize articles. Below is a single, complete data point from this dataset, formatted as a continuous block of text. Based on the typical structure for such training data, which option correctly identifies the two primary components of this sample?
Data Sample:
System: You are a helpful assistant that summarizes texts.
Instruction: Summarize the following article.
Article: [A long article about the discovery of a new planet...]
Summary: Scientists have discovered a new exoplanet, named Kepler-186f, which orbits within the habitable zone of its star, suggesting the potential for liquid water.
A developer is creating a single data sample to fine-tune a chatbot. Arrange the following components into the correct sequence to form a complete and valid training example.
Constructing a Fine-Tuning Sample
Examples of Instruction Fine-Tuning Samples
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System Information in LLM Inputs
A developer is preparing a data sample to train a language model to act as a helpful, concise code reviewer. The complete input for the model is constructed by combining several pieces of text. Match each component name on the left with its corresponding text from the input sample on the right.
Diagnosing Fine-Tuning Input Flaws
A developer is creating a data sample to fine-tune a language model. The complete input for the model is shown below. Analyze the text and identify the part that constitutes the 'system information', which sets the overall behavior and constraints for the model.
Input Text: "You are a helpful travel agent. Your goal is to find the most affordable travel options in Europe. Do not suggest any destination outside of Europe. Based on the user's request, provide a 3-day itinerary. User request: I want to visit Italy for 3 days on a budget of $500."