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Input and Output Sequences in SFT
Constructing an SFT Training Pair for Text Summarization
A developer is creating a dataset to fine-tune a language model for text summarization. Below is a source text and the desired summary. Your task is to define the 'input sequence' and the 'output sequence' that would constitute a single training example for this Supervised Fine-Tuning task.
Source Text: "Quantum computing is an emerging field that harnesses the principles of quantum mechanics to solve problems too complex for classical computers. Unlike classical bits, which can be a 0 or a 1, quantum bits or 'qubits' can exist in a superposition of both states simultaneously. This property, along with entanglement, allows quantum computers to perform a vast number of calculations at once."
Desired Summary: "Quantum computing uses quantum mechanics principles like superposition and entanglement, allowing its 'qubits' to process vast calculations simultaneously, tackling problems beyond the reach of classical computers."
Based on the above, what would be the input sequence and the output sequence?
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1
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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
Ch.2 Generative Models - Foundations of Large Language Models
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Dataset Composition for RL Fine-Tuning in RLHF
A machine learning engineer is creating a dataset to fine-tune a language model to act as a helpful assistant. The goal is to teach the model to follow instructions and provide complete, high-quality answers. Which of the following examples represents the most effective input-output pair for this supervised fine-tuning task?
Structuring a Sample from Input and Output Segments
Deconstructing an SFT Training Sample
Constructing an SFT Training Pair for Text Summarization