For generative AIs to be trustworthy, establishing transparent common grounding with humans is essential. As a preparation toward human-model common grounding, this study examines the process of model-model common grounding. In this context, common ground is defined as a cognitive framework shared among agents in communication, enabling the connection of symbols exchanged between agents to the meanings inherent in each agent. This connection is facilitated by a shared cognitive framework among the agents involved. In this research, we focus on the tangram naming task (TNT) as a testbed to examine the common-ground-building process. Unlike previous models designed for this task, our approach employs generative AIs to visualize the internal processes of the model. In this task, the sender constructs a metaphorical image of an abstract figure within the model and generates a detailed description based on this image. The receiver interprets the generated description from the partner by constructing another image and reconstructing the original abstract figure. Preliminary results from the study show an improvement in task performance beyond the chance level, indicating the effect of the common cognitive framework implemented in the models. Additionally, we observed that incremental backpropagations leveraging successful communication cases for a component of the model led to a statistically significant increase in performance. These results provide valuable insights into the mechanisms of common grounding made by generative AIs, improving human communication with the evolving intelligent machines in our future society.

翻译：为使生成式AI具有可信赖性，建立与人类透明的共同基础至关重要。作为实现人机共同基础的预备研究，本文考察了模型间共同基础的构建过程。在此语境下，共同基础被定义为通信主体间共享的认知框架，该框架能将主体间交换的符号与各自主体内涵意义相联结，这种联结通过主体间共享的认知框架得以实现。本研究以七巧板命名任务（TNT）为测试平台，探究共同基础构建过程。与既有任务模型不同，本方法采用生成式AI可视化模型内部进程。在该任务中，发送方在模型内部构建抽象图形的隐喻图像，并据此生成详细描述；接收方则通过构建对应图像解读伙伴生成的描述，进而重建原始抽象图形。初步实验结果显示，任务表现显著优于随机水平，证实了模型中实现的共享认知框架的有效性。此外，我们发现基于成功通信案例对模型组件进行增量反向传播，可导致性能出现统计显著性提升。这些结果为理解生成式AI建立共同基础的机制提供了重要见解，将有助于改善未来社会中人类与不断进化的智能机器之间的沟通。