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 versions 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 of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly produce multiple variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text segments for rapid in-place comparisons using mouse-over interactions on a context 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：通过重写文本来探索替代思路是写作过程的核心部分。最先进的大语言模型（LLM）能简化写作变体的生成过程。然而，当前界面在同时考虑多种变体时存在挑战：创建新版本时难以避免覆盖原文，而顺序粘贴变体会导致文档混乱，增加工作负荷并干扰作者的写作流畅性。为解决此问题，我们提出ABScribe——一种支持在人类-AI协作写作任务中快速且视觉化探索写作变体的界面。使用ABScribe，用户可通过LLM提示快速生成多个变体，这些提示会自动转换为可复用的按钮。变体以相邻方式存储在文本片段内，用户可通过上下文工具栏上的鼠标悬停交互进行快速原位比较。我们开展的12名写作者用户研究表明，与流行的基线工作流程相比，ABScribe显著降低了任务工作负荷（d = 1.20，p < 0.001），提升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探索变体的方式。