bibtext: @inproceedings{
  yang2024model,
  title={Model Autophagy Analysis to Explicate Self-consumption within Human-{AI} Interactions},
  author={Shu Yang and Muhammad Asif Ali and Lu Yu and Lijie Hu and Di Wang},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=FX4fUThO9H}
  }
abstract: 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 present Model Autophagy Analysis for large models’ selfconsumption explanation. We employ 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.
title: Model Autophagy Analysis to Explicate Self-consumption within Human-AI Interactions
authors: Shu Yang Muhammad Asif Ali Lu Yu Lijie Hu Di Wang