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). If xsample is 'Instruction: Translate to Spanish. Input: Hello.' and ysample is '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?