  Generative AI technologies have been deployed in many places, such as
(multimodal) large language models and vision generative models. Their
remarkable performance should be attributed to massive training data and
emergent reasoning abilities. However, the models would memorize and generate
sensitive, biased, or dangerous information originated from the training data
especially those from web crawl. New machine unlearning (MU) techniques are
being developed to reduce or eliminate undesirable knowledge and its effects
from the models, because those that were designed for traditional
classification tasks could not be applied for Generative AI. We offer a
comprehensive survey on many things about MU in Generative AI, such as a new
problem formulation, evaluation methods, and a structured discussion on the
advantages and limitations of different kinds of MU techniques. It also
presents several critical challenges and promising directions in MU research. A
curated list of readings can be found:
https://github.com/franciscoliu/GenAI-MU-Reading.
