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.

通过研究模型间共同基准的过程，该研究关注以生成 AI 为基础在人工智能模型中建立共同基准的过程，其中以七巧板命名任务作为测试实验，并观察到通过增量反向传播可以提高模型性能。