Deconstructing a Fine-Tuning Sample
A language model is being fine-tuned using data samples structured as a single sequence. Consider the following example sequence: ['The capital of France is Paris.', 'Instruction: What is the capital of France?']. Based on the conventional structure for such samples, identify the input segment (xsample) and the output segment (ysample). Briefly explain your reasoning.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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Selective Gradient Propagation for Sub-sequence Loss
Sample-wise Negative Log-Likelihood Loss for a Sub-sequence
For a supervised fine-tuning task, a single training instance consists of an input segment (
xsample) and a corresponding output segment (ysample). Ifxsampleis 'Instruction: Translate to Spanish. Input: Hello.' andysampleis 'Response: Hola.', which of the following represents the correct structure for the final combined sample that the model will process?Deconstructing a Fine-Tuning Sample
In preparing a data sample for supervised fine-tuning, a common practice is to structure the sample by concatenating the output segment (
ysample) and the input segment (xsample) into a single sequence:sample = [ysample, xsample]. What is the primary reason for placing the output segment before the input segment in this structure?