CatAlyst uses generative models to help workers' progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst's effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers' digital well-being.
翻译:催化者使用基因化模型来影响工人的任务参与,而不是直接促进他们的任务产出,从而帮助工人的进步。它促使分心的工人通过继续工作恢复工作任务,并将其作为一种比常规(预先确定的)反馈更有背景意识的干预。 及时性可以通过吸引他们的兴趣和降低恢复工作的障碍来发挥作用,即使所创造的继续工作不足以替代他们的工作,而最近旨在替代工作的人类-大赦国际合作研究则取决于稳定的高精确度。这免除了对具体领域模型的调整,使之适用于各种任务。我们有关写作和幻灯片编辑工作的研究表明,Caterest在帮助工人迅速恢复任务方面的效力,而认知负荷则降低。结果表明,在一种新型的人类-AI合作形式中,公开存在的、但每个领域不完善的大型典型模型可以促进工人的数字福祉。