While alignment algorithms are now commonly used to tune pre-trained language models towards a user's preferences, we lack explanations for the underlying mechanisms in which models become ``aligned'', thus making it difficult to explain phenomena like jailbreaks. In this work we study a popular algorithm, direct preference optimization (DPO), and the mechanisms by which it reduces toxicity. Namely, we first study how toxicity is represented and elicited in a pre-trained language model, GPT2-medium. We then apply DPO with a carefully crafted pairwise dataset to reduce toxicity. We examine how the resulting model averts toxic outputs, and find that capabilities learned from pre-training are not removed, but rather bypassed. We use this insight to demonstrate a simple method to un-align the model, reverting it back to its toxic behavior.
@article{arxiv.2401.01967,
title = {A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity},
author = {Andrew Lee and Xiaoyan Bai and Itamar Pres and Martin Wattenberg and Jonathan K. Kummerfeld and Rada Mihalcea},
journal= {arXiv preprint arXiv:2401.01967},
year = {2024}
}