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：CatAlyst通过影响工作者的任务参与度而非直接贡献于任务输出，借助生成模型促进其工作进展。该模型通过生成工作延续内容并呈现为比传统（预设）反馈更具上下文感知能力的干预措施，促使分心的工作者恢复任务。即使生成的延续内容不足以替代其工作，该提示仍能通过吸引其兴趣并降低恢复门槛发挥作用，而近期以工作替代为目标的人机协作研究则依赖于稳定的高精度。这使得CatAlyst免于领域特定的模型调优，并可应用于多种任务。我们开展的涉及写作与幻灯片编辑任务的研究表明，CatAlyst能够有效帮助工作者在降低认知负荷的情况下快速恢复任务。该结果揭示了一种新型人机协作形式：广泛可用但并非在每个个体领域都完美的大型生成模型，可为工作者的数字福祉做出贡献。