发表机构
Cornell Tech; Harvard University（康奈尔科技学院; 哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DimSteer通过自动发现并展示风格控制滑块，减少用户对提示回忆的依赖，实验表明其降低认知负担并提升识别式探索。研究验证了提示局部控制的有效性。
AI 中文摘要
大语言模型写作界面通常要求用户通过反复用自然语言描述期望的修改来引导输出。然而，作者往往只有在看到备选方案后才能识别出有用的风格方向，这使得修订过程高度依赖回忆。我们提出了DimSteer，一种写作界面，它采样提示局部的补全，发现激活空间中高方差的变异轴，为其标注标签，并将其展示为带有极点预览、差异比较和重置控制的滑块。用户可以操作这些已发现的维度，从而减少每次风格调整时重新表述提示的需求。在一项包含16名参与者、以匹配的仅提示基线为对照的受试者内研究中，DimSteer降低了脑力需求、努力程度和挫败感，同时保持了可比的感知成功度。参与者重视这些被揭示的维度，但16人中有15人表示他们不会想到在提示中请求同样的修改。结果表明，提示局部控制可以将大语言模型写作从基于回忆的提示转向基于识别的探索和直接操作，同时保留提示用于开放式编辑。
英文摘要
Large language model writing interfaces often make users steer outputs by repeatedly articulating desired changes in natural language. Yet writers may recognize useful stylistic directions only after seeing alternatives, making revision recall-heavy. We present DimSteer, an authoring interface that samples prompt-local completions, discovers high-variance activation-space axes of variation, labels them, and exposes them as sliders with pole previews, diff comparison, and reset controls. Users can manipulate discovered dimensions, reducing the need to reformulate prompts for each stylistic adjustment. In a within-subjects study with 16 participants against a matched prompt-only baseline, DimSteer reduced mental demand, effort, and frustration while preserving comparable perceived success. Participants valued the surfaced dimensions, yet 15 of 16 disagreed that they would have thought to request the same changes in a prompt. Results suggest prompt-local controls can shift LLM authoring from recall-based prompting toward recognition-based exploration and direct manipulation, while preserving prompting for open-ended edits.
Comments24 pages, 5 figures
