Explanations form the foundation of knowledge sharing and build upon
communication principles, social dynamics, and learning theories. We focus
specifically on conversational approaches for explanations because the context
is highly adaptive and interactive. Our research leverages previous work on
explanatory acts, a framework for understanding the different strategies that
explainers and explainees employ in a conversation to both explain, understand,
and engage with the other party. We use the 5-Levels dataset was constructed
from the WIRED YouTube series by Wachsmuth et al., and later annotated by
Booshehri et al. with explanatory acts. These annotations provide a framework
for understanding how explainers and explainees structure their response when
crafting a response.
With the rise of generative AI in the past year, we hope to better understand
the capabilities of Large Language Models (LLMs) and how they can augment
expert explainer's capabilities in conversational settings. To achieve this
goal, the 5-Levels dataset (We use Booshehri et al.'s 2023 annotated dataset
with explanatory acts.) allows us to audit the ability of LLMs in engaging in
explanation dialogues. To evaluate the effectiveness of LLMs in generating
explainer responses, we compared 3 different strategies, we asked human
annotators to evaluate 3 different strategies: human explainer response, GPT4
standard response, GPT4 response with Explanation Moves.

这篇研究论文探讨了解释的重要性和基于对话的方法，使用了大型语言模型（LLMs）来增强专家解释者在对话环境中的能力，并通过评估不同策略的效果来了解 LLMs 在生成解释性回应方面的能力。

ChatGPT 与我的教授相比，是更好的解释者吗？：评估 LLMs 在对话中的解释能力与人类基准

"Is ChatGPT a Better Explainer than My Professor?": Evaluating the  Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline

Social robots aim to establish long-term bonds with humans through engaging
conversation. However, traditional conversational approaches, reliant on
scripted interactions, often fall short in maintaining engaging conversations.
This paper addresses this limitation by integrating large language models
(LLMs) into social robots to achieve more dynamic and expressive conversations.
We introduce a fully-automated conversation system that leverages LLMs to
generate robot responses with expressive behaviors, congruent with the robot's
personality. We incorporate robot behavior with two modalities: 1) a
text-to-speech (TTS) engine capable of various delivery styles, and 2) a
library of physical actions for the robot. We develop a custom,
state-of-the-art emotion recognition model to dynamically select the robot's
tone of voice and utilize emojis from LLM output as cues for generating robot
actions. A demo of our system is available here. To illuminate design and
implementation issues, we conduct a pilot study where volunteers chat with a
social robot using our proposed system, and we analyze their feedback,
conducting a rigorous error analysis of chat transcripts. Feedback was
overwhelmingly positive, with participants commenting on the robot's empathy,
helpfulness, naturalness, and entertainment. Most negative feedback was due to
automatic speech recognition (ASR) errors which had limited impact on
conversations. However, we observed a small class of errors, such as the LLM
repeating itself or hallucinating fictitious information and human responses,
that have the potential to derail conversations, raising important issues for
LLM application.

通过将大型语言模型（LLMs）整合到社交机器人中，本研究旨在解决传统基于脚本互动的对话方法在维持有趣的对话方面存在的局限性。我们引入了一个完全自动化的对话系统，利用 LLMs 生成具有有表情的机器人回应，与机器人的个性一致。我们结合两种模式的机器人行为：1）具有多种表现风格的文本转语音（TTS）引擎，和 2）用于机器人的动作库。我们开发了一个定制的最先进的情感识别模型，用于动态选择机器人的语调，并利用 LLM 输出中的表情符号作为生成机器人动作的线索。通过一个志愿者与社交机器人进行对话的试验，我们研究和分析了他们的反馈，对聊天记录进行了严格的错误分析，以阐明设计和实现问题。反馈普遍积极，参与者评论机器人具有共情力、乐于助人、自然和娱乐性。最负面的反馈是由于自动语音识别（ASR）错误，这对对话的影响有限。然而，我们观察到了一类较小的错误，如 LLM 的自我重复或虚构信息和人类回答的产生，这可能会破坏对话，引发了 LLM 应用中的重要问题。

使用 LLMs 生成饶有表情的机器人行为与桌面机器人 Haru 的对话

Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in  Conversations with the Tabletop Robot Haru

Business processes are commonly represented by modelling languages, such as
Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the
most popular standard notation for modelling business processes, the Business
Process Model and Notation (BPMN). Most recently, chatbots, programs that allow
users to interact with a machine using natural language, have been increasingly
used for business process execution support. A recent category of chatbots
worth mentioning is generative-based chatbots, powered by Large Language Models
(LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and
Google's Pathways Language Model (PaLM), which are trained on billions of
parameters and support conversational intelligence. However, it is not clear
whether generative-based chatbots are able to understand and meet the
requirements of constructs such as those provided by BPMN for process execution
support. This paper presents a case study to compare the performance of
prominent generative models, GPT and PaLM, in the context of process execution
support. The research sheds light into the challenging problem of using
conversational approaches supported by generative chatbots as a means to
understand process-aware modelling notations and support users to execute their
tasks.

该论文通过案例研究比较了基于生成模型的聊天机器人（如 GPT 和 PaLM）在过程执行支持中的表现，揭示了使用会话式方法支持生成聊天机器人作为理解过程感知建模符号并协助用户执行任务的挑战性问题。