Throughout a conversation, the way participants interact with each other is
in constant flux: their tones may change, they may resort to different
strategies to convey their points, or they might alter their interaction
patterns. An understanding of these dynamics can complement that of the actual
facts and opinions discussed, offering a more holistic view of the trajectory
of the conversation: how it arrived at its current state and where it is likely
heading.
In this work, we introduce the task of summarizing the dynamics of
conversations, by constructing a dataset of human-written summaries, and
exploring several automated baselines. We evaluate whether such summaries can
capture the trajectory of conversations via an established downstream task:
forecasting whether an ongoing conversation will eventually derail into toxic
behavior. We show that they help both humans and automated systems with this
forecasting task. Humans make predictions three times faster, and with greater
confidence, when reading the summaries than when reading the transcripts.
Furthermore, automated forecasting systems are more accurate when constructing,
and then predicting based on, summaries of conversation dynamics, compared to
directly predicting on the transcripts.

我们引入了动态对话总结的任务，通过构建一个人为编写的总结数据集并探索几种自动化基准来总结对话的动态。我们评估了这些总结是否能够通过一个已建立的下游任务 —— 预测正在进行的对话是否最终会发展成有害行为 —— 来捕捉对话的轨迹。我们证明这些总结对人类和自动化系统在预测任务上都有帮助。人们在阅读总结时，做出预测的速度是在阅读完整对话的三倍，并且更有信心。此外，与直接对对话记录进行预测相比，自动化预测系统在构建并基于对话动态总结进行预测时更加准确。

我们是如何到达这里的？概述会话动态

How Did We Get Here? Summarizing Conversation Dynamics

Recently, research in human-robot interaction began to consider a robot's
influence at the group level. Despite the recent growth in research
investigating the effects of robots within groups of people, our overall
understanding of what happens when robots are placed within groups or teams of
people is still limited. This paper investigates several key problems for soci
robots that manage conversations in a group setting, where the number of
participants is more than two. In a group setting, the conversation dynamics
are a lot more complicated than the conventional one-to-one conversation, thus,
there are more challenges need to be solved.

本文研究了群组环境中机器人管理对话时遇到的几个关键问题，包括人机交互、群体层面、社交机器人、对话动力学和群组设置，以及各种需要解决的挑战。

社会机器人在群体对话中的行为研究

A Study on Social Robot Behavior in Group Conversation

Since its launch, ChatGPT has achieved remarkable success as a versatile
conversational AI platform, drawing millions of users worldwide and garnering
widespread recognition across academic, industrial, and general communities.
This paper aims to point a portrait of early GPT users and understand how they
evolved. Specific questions include their topics of interest and their
potential careers; and how this changes over time. We conduct a detailed
analysis of real-world ChatGPT datasets with multi-turn conversations between
users and ChatGPT. Through a multi-pronged approach, we quantify conversation
dynamics by examining the number of turns, then gauge sentiment to understand
user sentiment variations, and finally employ Latent Dirichlet Allocation (LDA)
to discern overarching topics within the conversation. By understanding shifts
in user demographics and interests, we aim to shed light on the changing nature
of human-AI interaction and anticipate future trends in user engagement with
language models.

本文通过对实际的 ChatGPT 数据集进行分析，研究 ChatGPT 的早期用户的兴趣话题、潜在职业以及这些因素随时间的变化，以理解人工智能与人类互动的变化以及语言模型用户参与的未来趋势。