# MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions

arXiv: https://arxiv.org/abs/2402.11271 · HTML: https://mlsanity.com/paper/2402.11271/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [An LLM can Fool Itself: A Prompt-Based Adversarial Attack](https://mlsanity.com/paper/2310.13345/) (2023)
- [Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks](https://mlsanity.com/paper/2306.07899/) (2023)
- [LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores](https://mlsanity.com/paper/2311.09766/) (2023)
- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Style Over Substance: Evaluation Biases for Large Language Models](https://mlsanity.com/paper/2307.03025/) (2023)
- [High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data](https://mlsanity.com/paper/2107.11136/) (2021)
- [SEAT: Stable and Explainable Attention](https://mlsanity.com/paper/2211.13290/) (2022)
- [Differentially Private Natural Language Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2301.09112/) (2023)
- [Practical Differentially Private and Byzantine-resilient Federated Learning](https://mlsanity.com/paper/2304.09762/) (2023)
- [Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns](https://mlsanity.com/paper/2305.04812/) (2023)
- [Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations](https://mlsanity.com/paper/2004.03554/) (2020)
- [Faster Rates of Private Stochastic Convex Optimization](https://mlsanity.com/paper/2108.00331/) (2021)
- [Differentially Private Non-convex Learning for Multi-layer Neural Networks](https://mlsanity.com/paper/2310.08425/) (2023)
- [Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data](https://mlsanity.com/paper/1910.00482/) (2019)
- [GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models](https://mlsanity.com/paper/2203.13397/) (2022)

## Built on by (most cited first, 7 of 7)

- [Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy](https://mlsanity.com/paper/2402.19379/) (2024)
- [Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements](https://mlsanity.com/paper/2502.12904/) (2025)
- [A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2406.12255/) (2024)
- [Delving into: the quantification of Ai-generated content on the internet (synthetic data)](https://mlsanity.com/paper/2504.08755/) (2025)
- [Rethinking Prompt-based Debiasing in Large Language Models](https://mlsanity.com/paper/2503.09219/) (2025)
- [Information Retrieval in the Age of Generative AI: The RGB Model](https://mlsanity.com/paper/2504.20610/) (2025)
- [Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images](https://mlsanity.com/paper/2506.07184/) (2025)

## Abstract (arXiv)

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.