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

Compositional generalization refers to a model's capability to generalize to
newly composed input data based on the data components observed during
training. It has triggered a series of compositional generalization analysis on
different tasks as generalization is an important aspect of language and
problem solving skills. However, the similar discussion on math word problems
(MWPs) is limited. In this manuscript, we study compositional generalization in
MWP solving. Specifically, we first introduce a data splitting method to create
compositional splits from existing MWP datasets. Meanwhile, we synthesize data
to isolate the effect of compositions. To improve the compositional
generalization in MWP solving, we propose an iterative data augmentation method
that includes diverse compositional variation into training data and could
collaborate with MWP methods. During the evaluation, we examine a set of
methods and find all of them encounter severe performance loss on the evaluated
datasets. We also find our data augmentation method could significantly improve
the compositional generalization of general MWP methods. Code is available at
this https URL.

本文研究了数学单词问题中的组合泛化，并提出了一种迭代的数据增强方法，将多种组合变化引入到训练数据中，从而显著提高了常规数学单词问题方法的组合泛化能力。