  The increasing significance of large models and their multi-modal variants in
societal information processing has ignited debates on social safety and
ethics. However, there exists a paucity of comprehensive analysis for: (i) the
interactions between human and artificial intelligence systems, and (ii)
understanding and addressing the associated limitations. To bridge this gap, we
propose Model Autophagy Analysis (MONAL) for large models' self-consumption
explanation. MONAL employs two distinct autophagous loops (referred to as
``self-consumption loops'') to elucidate the suppression of human-generated
information in the exchange between human and AI systems. Through comprehensive
experiments on diverse datasets, we evaluate the capacities of generated models
as both creators and disseminators of information. Our key findings reveal (i)
A progressive prevalence of model-generated synthetic information over time
within training datasets compared to human-generated information; (ii) The
discernible tendency of large models, when acting as information transmitters
across multiple iterations, to selectively modify or prioritize specific
contents; and (iii) The potential for a reduction in the diversity of socially
or human-generated information, leading to bottlenecks in the performance
enhancement of large models and confining them to local optima.
