IfYouDontUnderstandItDontUseIt

A short exporation and paper on an approach to AI safety in which we attempt train autoencoder-like filters on clean data to avoid trojans. The broad goal is some form of soft enumerative safety by basically saying "only things in this dataset are OK" and the proposed approach is to train SAEs or low rank replacements or normal replacements to replace layers.

The main work is in src/. Instructions for running are there.

Some of the other stuff from before includes

• notes/ includes things that I thought were noteworthy to take note of. Sometimes there will be numbers but normally this is qualitative experience.
• There are previous iterations of this work in other branches (mainly adriano/scratch contains unmaintained scripts that were refactored to become what we have here today)
• experiment_results_2024_07_08 is a consolidation of experiment result (YAML) files that can be used (plug and play) to reproduce the results here