Large Language Models(LLMs) have dramatically revolutionized the field of
Natural Language Processing(NLP), offering remarkable capabilities that have
garnered widespread usage. However, existing interaction paradigms between LLMs
and users are constrained by either inflexibility, limitations in
customization, or a lack of persistent learning. This inflexibility is
particularly evident as users, especially those without programming skills,
have restricted avenues to enhance or personalize the model. Existing
frameworks further complicate the model training and deployment process due to
their computational inefficiencies and lack of user-friendly interfaces. To
overcome these challenges, this paper introduces a novel interaction
paradigm-'Online Training using External Interactions'-that merges the benefits
of persistent, real-time model updates with the flexibility for individual
customization through external interactions such as AI agents or online/offline
knowledge bases.

大型语言模型 (LLMs) 在自然语言处理 (NLP) 领域产生了巨大的革命，提供了引人注目的功能，得到了广泛的使用。然而，现有的 LLMs 与用户之间的交互范式存在着局限性、定制能力不足或者缺乏持续学习的问题。为了克服这些挑战，本文介绍了一种新的交互范式 -' 使用外部交互进行在线训练 '，通过与 AI 代理或在线 / 离线知识库等外部交互，将持续实时模型更新的好处与个性化定制的灵活性相结合。

大规模语言模型的在线训练：聊天中学习

Online Training of Large Language Models: Learn while chatting

Thanks to their generative capabilities, large language models (LLMs) have
become an invaluable tool for creative processes. These models have the
capacity to produce hundreds and thousands of visual and textual outputs,
offering abundant inspiration for creative endeavors. But are we harnessing
their full potential? We argue that current interaction paradigms fall short,
guiding users towards rapid convergence on a limited set of ideas, rather than
empowering them to explore the vast latent design space in generative models.
To address this limitation, we propose a framework that facilitates the
structured generation of design space in which users can seamlessly explore,
evaluate, and synthesize a multitude of responses. We demonstrate the
feasibility and usefulness of this framework through the design and development
of an interactive system, Luminate, and a user study with 8 professional
writers. Our work advances how we interact with LLMs for creative tasks,
introducing a way to harness the creative potential of LLMs.

通过提出一个框架，使用户能够无缝地探索、评估和综合多种响应，并通过 Luminate 互动系统与 8 名专业作家的用户研究展示了这个框架的可行性和有用性，我们推进了与大型语言模型的创造性任务互动的方式，引入了一种利用大型语言模型的创造潜力的方法。

利用大型语言模型进行结构化设计空间的生成和探索，用于人机共创

Structured Generation and Exploration of Design Space with Large  Language Models for Human-AI Co-Creation

Publicly deploying research chatbots is a nuanced topic involving necessary
risk-benefit analyses. While there have recently been frequent discussions on
whether it is responsible to deploy such models, there has been far less focus
on the interaction paradigms and design approaches that the resulting
interfaces should adopt, in order to achieve their goals more effectively. We
aim to pose, ground, and attempt to answer HCI questions involved in this
scope, by reporting on a mixed-methods user study conducted on a recent
research chatbot. We find that abstract anthropomorphic representation for the
agent has a significant effect on user's perception, that offering AI
explainability may have an impact on feedback rates, and that two (diegetic and
extradiegetic) levels of the chat experience should be intentionally designed.
We offer design recommendations and areas of further focus for the research
community.

这篇研究提供了与研究聊天机器人交互方式和设计途径有关的 HCI 问题的见解和建议，并报告了最近进行的一项混合方法用户研究的结果。

研究聊天机器人的公开部署的人机交互方面：用户研究、设计建议和开放挑战

The HCI Aspects of Public Deployment of Research Chatbots: A User Study,  Design Recommendations, and Open Challenges

Human-centered AI workflows involve stakeholders with multiple roles
interacting with each other and automated agents to accomplish diverse tasks.
In this paper, we call for a holistic view when designing support mechanisms,
such as interaction paradigms, interfaces, and systems, for these multifaceted
workflows.

本篇论文呼吁在设计支持人机互动的多领域工作流时采用全面的视角，包括互动范式、界面和系统等支持机制。