  Generative artificial intelligence (AI) technologies and large models are
producing realistic outputs across various domains, such as images, text,
speech, and music. Creating these advanced generative models requires
significant resources, particularly large and high-quality datasets. To
minimize training expenses, many algorithm developers use data created by the
models themselves as a cost-effective training solution. However, not all
synthetic data effectively improve model performance, necessitating a strategic
balance in the use of real versus synthetic data to optimize outcomes.
  Currently, the previously well-controlled integration of real and synthetic
data is becoming uncontrollable. The widespread and unregulated dissemination
of synthetic data online leads to the contamination of datasets traditionally
compiled through web scraping, now mixed with unlabeled synthetic data. This
trend portends a future where generative AI systems may increasingly rely
blindly on consuming self-generated data, raising concerns about model
performance and ethical issues. What will happen if generative AI continuously
consumes itself without discernment? What measures can we take to mitigate the
potential adverse effects?
  There is a significant gap in the scientific literature regarding the impact
of synthetic data use in generative AI, particularly in terms of the fusion of
multimodal information. To address this research gap, this review investigates
the consequences of integrating synthetic data blindly on training generative
AI on both image and text modalities and explores strategies to mitigate these
effects. The goal is to offer a comprehensive view of synthetic data's role,
advocating for a balanced approach to its use and exploring practices that
promote the sustainable development of generative AI technologies in the era of
large models.
