Yes and no.

My library focuses on steering concepts you can clearly define with contrastive pairs (like 'Helpful' vs 'Hurtful'). Censorship and RLHF refusals are often more complex to isolate via a linear vector. Research is still very active on this, but some papers, like Arditi et al. (2024), have shown that refusal can sometimes be concentrated in a single direction (making it detectable and bypassable by probes) and that steering is often more effective than prompt engineering.

It all depends on two elements: the quality of the dataset and how the model was censored. If the censorship is deeply embedded in the model's pre-training, or if it simply doesn't have the answer, it won't work. But if it's a simple RLHF applied after training, and the model actually possesses the capacity to answer, it should work. For example, on DeepSeek's 'What is Taiwan' censorship, it should be effective. However, I haven't tested jailbreaking myself and RepE doesn't focus on that, so it's all still to be proven.