Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers' flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs.

翻译：通过重写文本探索替代想法是写作过程的核心。最先进的大型语言模型（LLMs）可简化写作变体的生成。然而，当前界面在同时处理多个变体时存在挑战：在不覆盖原文的情况下创建新变体较为困难，而将变体依次粘贴则会导致文档杂乱，增加工作负载并打断写作者的思路。为解决这一问题，我们提出ABScribe界面，该界面支持在人类-AI协作写作任务中快速且视觉化地探索与组织写作变体。借助ABScribe，用户可通过LLM提示快速修改变体，这些提示将自动转化为可复用的按钮。变体被存储在文本字段的邻近位置，用户可通过弹出工具栏上的鼠标悬停操作进行快速原位比较。我们对12位写作者的用户研究表明，与主流基线工作流程相比，ABScribe显著降低了任务工作负载（d=1.20，p<0.001），增强了用户对修订过程的感知（d=2.41，p<0.001），并揭示了写作者利用LLM探索变体的行为模式。