Indeed, it makes sense, and that’s certainly an alternative approach. However, including here the desired output in the model input serves a specific purpose.
By providing the desired output along with the instruction and input text, the model learns not only to generate text based on the input but also to explicitly understand what the correct output should be for that particular input and instruction. This essentially guides the models towards producing outputs that align more closely with the desired output during fine-tuning.
If we don’t include the desired output in the model input, the model would solely rely on comparing its generated output with the desired output after processing the input and instruction. While this approach can still work, when we provide the desired output as part of the input this can improve the learning process by giving the model more direct information about what it should aim to produce, and usually leads to faster and more accurate fine-tuning results.