ABScribe: Rapid Exploration Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models
arxiv(2023)
摘要
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.
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