Predictive suggestion systems offer contextually-relevant text entry completions. Existing approaches, like autofill, often excel in narrowly-defined domains but fail to generalize to arbitrary workflows. We introduce a conceptual framework to analyze the compound demands of a particular suggestion context, yielding unique opportunities for large language models (LLMs) to infer suggestions for a wide range of domain-agnostic form-filling tasks that were out of reach with prior approaches. We explore these opportunities in OmniFill, a prototype that collects multi-faceted context including browsing and text entry activity to construct an LLM prompt that offers suggestions in situ for arbitrary structured text entry interfaces. Through a user study with 18 participants, we found that OmniFill offered valuable suggestions and we identified four themes that characterize users' behavior and attitudes: an "opportunistic scrapbooking" approach; a trust placed in the system; value in partial success; and a need for visibility into prompt context.

翻译：预测性建议系统能够提供上下文相关的文本输入补全。现有方法（如自动填充）通常在狭义定义的领域中表现出色，但难以泛化到任意工作流程。我们引入了一个概念框架来分析特定建议场景的复合需求，从而为大型语言模型（LLM）推断广泛领域无关表单填充任务的建议创造了独特机遇，这些任务此前方法难以企及。我们在OmniFill原型中探索了这些机遇，该系统通过收集包括浏览和文本输入活动在内的多面上下文，构建LLM提示，为任意结构化文本输入界面提供即时建议。通过一项包含18名参与者的用户研究，我们发现OmniFill提供了有价值的建议，并归纳出表征用户行为与态度的四个主题：“机会主义剪贴簿”方法；对系统的信任；部分成功的价值；以及提示上下文的可见性需求。