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。

人类和生成型人工智能间共享共同基础的认知架构：唐纳姆命名任务中模型间互动的试验

Cognitive Architecture Toward Common Ground Sharing Among Humans and  Generative AIs: Trial on Model-Model Interactions in Tangram Naming Task

Recently, generative AIs like ChatGPT have become available to the wide
public. These tools can for instance be used by students to generate essays or
whole theses. But how does a teacher know whether a text is written by a
student or an AI? In our work, we explore traditional and new features to (1)
detect text generated by AI from scratch and (2) text rephrased by AI. Since we
found that classification is more difficult when the AI has been instructed to
create the text in a way that a human would not recognize that it was generated
by an AI, we also investigate this more advanced case. For our experiments, we
produced a new text corpus covering 10 school topics. Our best systems to
classify basic and advanced human-generated/AI-generated texts have F1-scores
of over 96%. Our best systems for classifying basic and advanced
human-generated/AI-rephrased texts have F1-scores of more than 78%. The systems
use a combination of perplexity, semantic, list lookup, error-based,
readability, AI feedback, and text vector features. Our results show that the
new features substantially help to improve the performance of many classifiers.
Our best basic text rephrasing detection system even outperforms GPTZero by
183.8% relative in F1-score.

我们的研究旨在探索传统和新的特征，以便 (1) 检测 AI 生成的文本和 (2) 由 AI 改写的文本。结果显示，新特征显著提高了许多分类器的性能。我们最好的基本文本改写检测系统在 F1 分数上超过了 GPTZero 的 183.8%。