  Generative AI (GenAI), which aims to synthesize realistic and diverse data
samples from latent variables or other data modalities, has achieved remarkable
results in various domains, such as natural language, images, audio, and
graphs. However, they also pose challenges and risks to data privacy, security,
and ethics. Machine unlearning is the process of removing or weakening the
influence of specific data samples or features from a trained model, without
affecting its performance on other data or tasks. While machine unlearning has
shown significant efficacy in traditional machine learning tasks, it is still
unclear if it could help GenAI become safer and aligned with human desire. To
this end, this position paper provides an in-depth discussion of the machine
unlearning approaches for GenAI. Firstly, we formulate the problem of machine
unlearning tasks on GenAI and introduce the background. Subsequently, we
systematically examine the limitations of machine unlearning on GenAI models by
focusing on the two representative branches: LLMs and image generative
(diffusion) models. Finally, we provide our prospects mainly from three
aspects: benchmark, evaluation metrics, and utility-unlearning trade-off, and
conscientiously advocate for the future development of this field.
