Text entry is an essential task in our day-to-day digital interactions. Numerous intelligent features have been developed to streamline this process, making text entry more effective, efficient, and fluid. These improvements include sentence prediction and user personalization. However, as deep learning-based language models become the norm for these advanced features, the necessity for data collection and model fine-tuning increases. These challenges can be mitigated by harnessing the in-context learning capability of large language models such as GPT-3.5. This unique feature allows the language model to acquire new skills through prompts, eliminating the need for data collection and fine-tuning. Consequently, large language models can learn various text prediction techniques. We initially showed that, for a sentence prediction task, merely prompting GPT-3.5 surpassed a GPT-2 backed system and is comparable with a fine-tuned GPT-3.5 model, with the latter two methods requiring costly data collection, fine-tuning and post-processing. However, the task of prompting large language models to specialize in specific text prediction tasks can be challenging, particularly for designers without expertise in prompt engineering. To address this, we introduce Promptor, a conversational prompt generation agent designed to engage proactively with designers. Promptor can automatically generate complex prompts tailored to meet specific needs, thus offering a solution to this challenge. We conducted a user study involving 24 participants creating prompts for three intelligent text entry tasks, half of the participants used Promptor while the other half designed prompts themselves. The results show that Promptor-designed prompts result in a 35% increase in similarity and 22% in coherence over those by designers.

翻译：文本输入是日常数字交互中的核心任务。目前已开发出众多智能特性来优化这一过程，使文本输入更高效、更流畅，包括句子预测和用户个性化。然而，随着基于深度学习的语言模型成为这些高级功能的标配，数据收集和模型微调的需求随之增加。通过利用GPT-3.5等大语言模型的上下文学习能力可缓解这些挑战。这一独特特性使语言模型能够通过提示习得新技能，无需数据收集和微调。由此，大语言模型可学习多种文本预测技术。我们初步证明，在句子预测任务中，仅通过提示GPT-3.5便超越了基于GPT-2的系统，且与微调后的GPT-3.5模型性能相当，而后两种方法均需高昂的数据收集、微调和后处理成本。然而，引导大语言模型专精于特定文本预测任务存在挑战，尤其对缺乏提示工程专业知识的设计者而言。为此，我们提出Promptor——一种主动与设计者交互的对话式提示生成代理。Promptor能自动生成满足特定需求的复杂提示，从而解决这一难题。我们开展了一项包含24名参与者的用户研究，要求他们为三类智能文本输入任务创建提示，其中一半参与者使用Promptor，另一半自行设计提示。结果显示，与设计者自行设计的提示相比，Promptor生成的提示在相似度上提升35%，在连贯性上提升22%。