用微小意图标签提升人机共创内容生成的精准度
Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows
- 提出意图标签机制，将创作意图拆分为细粒度原子单元
- 用户研究验证其在模糊场景下灵活表达意图的有效性
- 适合需要精细控制生成内容的创意工作者使用
尽管生成式AI（GenAI）在内容创作中潜力巨大，但用户常难以有效将其融入创作流程。核心挑战包括：生成内容与用户意图不一致（意图提取与对齐）、用户不确定如何向AI清晰传达意图（提示词构建），以及AI系统缺乏灵活性以支持多样化创作流程（工作流灵活性）。针对这些问题，我们设计了IntentTagger：一种基于意图标签的幻灯片生成系统。意图标签是小型、原子化的概念单元，用于探索人机协同创作中的细粒度、非线性微提示交互。12名参与者的用户研究揭示了在不同模糊程度下灵活表达意图的价值，以及元意图挖掘的优势与挑战。研究最后讨论了这些发现的广泛影响及面向生成式AI的内容创作工作流设计启示。
原文摘要 · Abstract (English)
Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows.
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