  Recent work has demonstrated that finetuning is a promising approach to
'unlearn' concepts from large language models. However, finetuning can be
expensive, as it requires both generating a set of examples and running
iterations of finetuning to update the model. In this work, we show that simple
guardrail-based approaches such as prompting and filtering can achieve
unlearning results comparable to finetuning. We recommend that researchers
investigate these lightweight baselines when evaluating the performance of more
computationally intensive finetuning methods. While we do not claim that
methods such as prompting or filtering are universal solutions to the problem
of unlearning, our work suggests the need for evaluation metrics that can
better separate the power of guardrails vs. finetuning, and highlights
scenarios where guardrails expose possible unintended behavior in existing
metrics and benchmarks.
