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 modify and lose specific content selectively. 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.

翻译：大型语言模型和多模态模型在社会信息处理中的重要性日益凸显，引发了关于社会安全与伦理的激烈讨论。然而，现有研究鲜有从人类与人工智能系统交互的综合视角分析这些局限。本研究探讨了人类与大模型作为通信关键环节时存在的偏见与偏好。为此，我们设计了一个多模态数据集及三项不同实验，用于评估生成模型作为信息生产者和传播者时的表现。主要发现表明：相较于人类生成的信息，合成信息更易被纳入模型训练数据集及信息传递过程。此外，大模型作为信息传输媒介时，会倾向于选择性修改或丢失特定内容。在概念层面，我们提出了两种符合现实的"自噬（自我消耗）"循环模型，用以解释人类与AI系统信息交换中人类生成信息被抑制的现象。我们将上述趋势导致的社交信息多样性下降及模型性能瓶颈，统一定义为大模型的局部最优现象。