The dynamics of human-AI communication have been reshaped by language models
such as ChatGPT. However, extant research has primarily focused on dyadic
communication, leaving much to be explored regarding the dynamics of human-AI
communication in group settings. The availability of multiple language model
chatbots presents a unique opportunity for scholars to better understand the
interaction between humans and multiple chatbots. This study examines the
impact of multi-chatbot communication in a specific persuasion setting:
promoting charitable donations. We developed an online environment that enables
multi-chatbot communication and conducted a pilot experiment utilizing two
GPT-based chatbots, Save the Children and UNICEF chatbots, to promote
charitable donations. In this study, we present our development process of the
multi-chatbot interface and present preliminary findings from a pilot
experiment. Analysis of qualitative and quantitative feedback are presented,
and limitations are addressed.

这项研究探讨了多聊天机器人通信对于在特定说服场景中促进慈善捐赠的影响，分析了质性和数量性反馈，并讨论了该研究的局限性。

设计和评估用于人工智能与人类交流的多聊天机器人界面：一项说服任务的初步发现

Designing and Evaluating Multi-Chatbot Interface for Human-AI  Communication: Preliminary Findings from a Persuasion Task

From navigation systems to smart assistants, we communicate with various AI
on a daily basis. At the core of such human-AI communication, we convey our
understanding of the AI's capability to the AI through utterances with
different complexities, and the AI conveys its understanding of our needs and
goals to us through system outputs. However, this communication process is
prone to failures for two reasons: the AI might have the wrong understanding of
the user and the user might have the wrong understanding of the AI. To enhance
mutual understanding in human-AI communication, we posit the Mutual Theory of
Mind (MToM) framework, inspired by our basic human capability of "Theory of
Mind." In this paper, we discuss the motivation of the MToM framework and its
three key components that continuously shape the mutual understanding during
three stages of human-AI communication. We then describe a case study inspired
by the MToM framework to demonstrate the power of MToM framework to guide the
design and understanding of human-AI communication.

本文探讨人工智能与人类之间的相互交流，并提出了 Mutual Theory of Mind (MToM) 框架，旨在提高双方互相理解的能力和效率，介绍了框架的三个主要组成部分，以及通过案例研究展示了 MToM 框架在引导人工智能设计和人 - AI 交流理解方面的重要性。

人工智能通信的互想理论

Mutual Theory of Mind for Human-AI Communication

Humans use commonsense reasoning (CSR) implicitly to produce natural and
coherent responses in conversations. Aiming to close the gap between current
response generation (RG) models and human communication abilities, we want to
understand why RG models respond as they do by probing RG model's understanding
of commonsense reasoning that elicits proper responses. We formalize the
problem by framing commonsense as a latent variable in the RG task and using
explanations for responses as textual form of commonsense. We collect 6k
annotated explanations justifying responses from four dialogue datasets and ask
humans to verify them and propose two probing settings to evaluate RG models'
CSR capabilities. Probing results show that models fail to capture the logical
relations between commonsense explanations and responses and fine-tuning on
in-domain data and increasing model sizes do not lead to understanding of CSR
for RG. We hope our study motivates more research in making RG models emulate
the human reasoning process in pursuit of smooth human-AI communication.

该研究旨在通过探究响应生成模型对常识推理的理解程度以诱发适当的响应来缩小当前响应生成模型和人类沟通能力之间的差距，探测 RG 模型的常识推理能力并提出了两个探针设置来评估 RG 模型的常识推理功能。结果表明，模型无法捕获常识解释和响应之间的逻辑关系，领域内数据的微调和增加模型规模并不能带来对 CSR 的理解，这一研究有望激励更多的研究使 RG 模型效仿人类推理过程以实现流畅的人工智能交流。