PromptCrafter：通过与大语言模型的混合主动对话构建文生图提示词
PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM
AI总结：
针对文生图提示词创建效率低的问题，提出混合主动系统PromptCrafter，通过与LLM的迭代对话、澄清问题回应及历史回退功能，帮助用户高效探索模型能力并构建提示词，同时介绍了其设计与后续计划。
摘要：
文生图生成模型能够基于单个提示词生成涵盖多种主题与风格的图像。近期研究提出了各类交互方法，帮助用户理解模型能力并加以运用。然而，如何支持用户高效探索模型能力、创建有效的提示词仍是开放性研究问题。本文提出PromptCrafter，这是一种新型混合主动系统，可逐步构建文生图提示词。通过迭代流程，用户能够高效探索模型能力并明确自身意图。PromptCrafter还支持用户通过回应大语言模型（Large Language Model, LLM）生成的各类澄清问题来优化提示词。最后，用户可通过查看工作历史回退到所需步骤。在这篇研讨会论文中，我们探讨了PromptCrafter的设计过程及后续研究计划。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
