Sounds like you want the model to consider the whole body of poetry it was trained on minus "The Road Not Taken"? (To get rid of preconceptions/biases I guess?)

I'm skeptical that LLMs have the ability to conditionally silence part of their training data in that way because they don't have any information on the provenance of their weights (i.e. they don't have a ledger of which weights were affected by which data points in the training process). I suspect that your prompt serves as a hint that the output with the highest likelihood is probably wrong, activating some sort of "contrarian" subnetwork or "second guess" subnetwork that steers predictions away from whatever would have had the highest likelihood otherwise.