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Input and Output Sequences in SFT
In Supervised Fine-Tuning, training data is structured as pairs of sequences. The input sequence, denoted as , typically consists of a combination of an instruction and user-provided context. The corresponding output sequence, denoted as , represents the desired response that the model is trained to generate.
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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
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Example of SFT Dataset Samples
Key Attributes of Effective SFT Datasets and Their Impact on LLM Performance
Input and Output Sequences in SFT
A team is preparing a dataset to fine-tune a pre-trained language model to follow specific instructions. Which of the following data entries best exemplifies the fundamental structure of a single sample in a Supervised Fine-Tuning (SFT) dataset?
Evaluating a Potential Fine-Tuning Dataset
Characteristics of SFT Datasets
Analyzing Data Samples for Instruction-Following
Learn After
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