Text-to-image generation model is able to generate images across a diverse
range of subjects and styles based on a single prompt. Recent works have
proposed a variety of interaction methods that help users understand the
capabilities of models and utilize them. However, how to support users to
efficiently explore the model's capability and to create effective prompts are
still open-ended research questions. In this paper, we present PromptCrafter, a
novel mixed-initiative system that allows step-by-step crafting of
text-to-image prompt. Through the iterative process, users can efficiently
explore the model's capability, and clarify their intent. PromptCrafter also
supports users to refine prompts by answering various responses to clarifying
questions generated by a Large Language Model. Lastly, users can revert to a
desired step by reviewing the work history. In this workshop paper, we discuss
the design process of PromptCrafter and our plans for follow-up studies.

通过 PromptCrafter 这一新型的混合主动式系统，用户可以逐步构建文本到图像的提示，从而有效地探索模型的能力和澄清意图。

PromptCrafter: 通过与 LLM 的混合主动对话来制作文本到图像提示

PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative  Dialogue with LLM

Scientists and science journalists, among others, often need to make sense of
a large number of papers and how they compare with each other in scope, focus,
findings, or any other important factors. However, with a large corpus of
papers, it's cognitively demanding to pairwise compare and contrast them all
with each other. Fully automating this review process would be infeasible,
because it often requires domain-specific knowledge, as well as understanding
what the context and motivations for the review are. While there are existing
tools to help with the process of organizing and annotating papers for
literature reviews, at the core they still rely on people to serially read
through papers and manually make sense of relevant information.
We present AVTALER, which combines peoples' unique skills, contextual
awareness, and knowledge, together with the strength of automation. Given a set
of comparable text excerpts from a paper corpus, it supports users in
sensemaking and contrasting paper attributes by interactively aligning text
excerpts in a table so that comparable details are presented in a shared
column. AVTALER is based on a core alignment algorithm that makes use of modern
NLP tools. Furthermore, AVTALER is a mixed-initiative system: users can
interactively give the system constraints which are integrated into the
alignment construction process.

AVTALER 是一种基于核心对齐算法和现代 NLP 工具的混合倡议系统，用于帮助人们比较文献综述和对齐文本语段，以支持文本数据的语义化和对比文献属性。

用于支持对平行文本集合进行意义构建的交互式用户界面

An Interactive UI to Support Sensemaking over Collections of Parallel Texts

Recent neural generation systems have demonstrated the potential for
procedurally generating game content, images, stories, and more. However, most
neural generation algorithms are "uncontrolled" in the sense that the user has
little say in creative decisions beyond the initial prompt specification.
Co-creative, mixed-initiative systems require user-centric means of influencing
the algorithm, especially when users are unlikely to have machine learning
expertise. The key to co-creative systems is the ability to communicate ideas
and intent from the user to the agent, as well as from the agent to the user.
Key questions in co-creative AI include: How can users express their creative
intentions? How can creative AI systems communicate their beliefs, explain
their moves, or instruct users to act on their behalf? When should creative AI
systems take initiative? The answer to such questions and more will enable us
to develop better co-creative systems that make humans more capable of
expressing their creative intents. We introduce CREATIVE-WAND, a customizable
framework for investigating co-creative mixed-initiative generation.
CREATIVE-WAND enables plug-and-play injection of generative models and
human-agent communication channels into a chat-based interface. It provides a
number of dimensions along which an AI generator and humans can communicate
during the co-creative process. We illustrate the CREATIVE-WAND framework by
using it to study one dimension of co-creative communication-global versus
local creative intent specification by the user-in the context of storytelling.

探讨了 “co-creative AI” 以及 “mixed-initiative system” 的概念，重点在于通过人工智能对话的形式，探讨人类和计算机之间的良好沟通，实现用户的创造力和计算机生成内容的混合应用，最后介绍了一个可插入式转基因生成模型和人工智能代理通信通道的聊天界面框架 -“CREATIVE-WAND”。

创意棒：一个研究协作创意环境中交流效应的系统

Creative Wand: A System to Study Effects of Communications in Co-Creative Settings

Proper statistical modeling incorporates domain theory about how concepts
relate and details of how data were measured. However, data analysts currently
lack tool support for recording and reasoning about domain assumptions, data
collection, and modeling choices in an integrated manner, leading to mistakes
that can compromise scientific validity. For instance, generalized linear
mixed-effects models (GLMMs) help answer complex research questions, but
omitting random effects impairs the generalizability of results. To address
this need, we present Tisane, a mixed-initiative system for authoring
generalized linear models with and without mixed-effects. Tisane introduces a
study design specification language for expressing and asking questions about
relationships between variables. Tisane contributes an interactive compilation
process that represents relationships in a graph, infers candidate statistical
models, and asks follow-up questions to disambiguate user queries to construct
a valid model. In case studies with three researchers, we find that Tisane
helps them focus on their goals and assumptions while avoiding past mistakes.

Tisane 是一种混合式互动系统，用于构建包括 GLMMs 在内的广义线性模型。Tisane 引入了一种研究设计规范语言，用于表达和探究变量之间的关系。通过交互式编译过程，Tisane 能够推断候选统计模型，并进一步澄清用户查询，以构建合法的模型。在案例研究中，研究人员使用 Tisane 可以更好地专注于他们的目标和假设，从而避免过去的错误。