Human-AI Interactions in the Communication Era: Autophagy Makes Large Models Achieving Local Optima

Anonymous

TL;DR: Our study investigates the concerning trend of mutual alignment and self-consumption in these models' construction and use, which may lead to a local optimum in AI and societal development.

Abstract: The increasing significance of large language and multimodal models in societal information processing has ignited debates on social safety and ethics. However, few studies have approached the analysis of these limitations from the comprehensive perspective of human and artificial intelligence system interactions. This study investigates biases and preferences when humans and large models are used as key links in communication. To achieve this, we design a multimodal dataset and three different experiments to evaluate generative models in their roles as producers and disseminators of information. Our main findings highlight that synthesized information is more likely to be incorporated into model training datasets and messaging than human-generated information. Additionally, large models, when acting as transmitters of information, tend to selectively modify and lose specific content. Conceptually, we present two realistic models of autophagic ("self-consumption") loops to account for the suppression of human-generated information in the exchange of information between humans and AI systems. We generalize the declining diversity of social information and the bottleneck in model performance caused by the above trends to the local optima of large models.

Paper Type: long

Research Area: Computational Social Science and Cultural Analytics

Contribution Types: Model analysis & interpretability, Data resources, Position papers, Theory

Languages Studied: English,Chinese