Guardrail Baselines for Unlearning in LLMs
Pratiksha Thaker Yash Maurya Virginia Smith
Recent work has demonstrated that fine-tuning is a promising approach to
`unlearn' concepts from large language models. However, fine-tuning can be
expensive, as it requires both generating a set of examples and running
iterations of fine-tuning 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 fine-tuning. We recommend that researchers
investigate these lightweight baselines when evaluating the performance of more
computationally intensive fine-tuning 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. fine-tuning, and highlights
scenarios where guardrails themselves may be advantageous for unlearning, such
as in generating examples for fine-tuning or unlearning when only API access is
available.