Benchmark for Reversal and Unlearning of Harmful Fine-Tuning

This project will be independently researched and produced by me, I'm the only person who will be contributing towards this project; without any peers. Safety mechanisms are evolving into reversal and machine unlearning methods such as cheap re-alignment, direction ablation, alignment gating etc, if a fine-tune makes a model perform worse or unsafe, these methods (in research) are aimed at undoing that particular capability which would remove the unsafe attribute and we can fine-tune in better ways than earlier. Current research from organizations show evidence of these attributes being suppressed rather than removing it, this would in-turn prove to be more harmful in case we rely on such results as the models could be termed as ticking time bombs or sleeper agents which will be activated for harm once those attributes are triggered.

This project is aimed at building the missing artifact, which would be an open, adversarial durability benchmark that stress tests reversal and unlearning methods against a decided standard suite which help decide what fixes or fine tuning genuinely hold true and which ones are cosmetic.

The output would ideally be a reusable evaluation harness and a diagnostic that predicts genuine vs superficial removal before an attack is run.