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：摘要：通过重写文本探索替代性是写作过程的核心环节。当前先进的大语言模型能够简化写作变体的生成过程。然而，现有交互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有文本，而顺序粘贴会导致文档杂乱，增加工作负荷并打断作者写作思路。为此，我们提出ABScribe——一种支持在人机协作写作任务中快速且视觉化探索写作变体的交互界面。用户可通过LLM提示词快速生成多种变体，这些提示词自动转换为可重用按钮。变体被相邻存储在文本段落中，通过上下文工具栏的鼠标悬停交互实现快速原位比较。我们开展的12位作者用户研究表明：相较于主流基线工作流，ABScribe显著降低了任务工作负荷（d=1.20，p<0.001），提升了用户对修订流程的感知（d=2.41，p<0.001），并揭示了作者利用大语言模型探索变体的行为模式。