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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 improvementsinclude sentence prediction and user personalization. However, as deeplearning-based language models become the norm for these advanced features, thenecessity for data collection and model fine-tuning increases. These challengescan be mitigated by harnessing the in-context learning capability of largelanguage models such as GPT-3.5. This unique feature allows the language modelto acquire new skills through prompts, eliminating the need for data collectionand fine-tuning. Consequently, large language models can learn various textprediction techniques. We initially showed that, for a sentence predictiontask, merely prompting GPT-3.5 surpassed a GPT-2 backed system and iscomparable with a fine-tuned GPT-3.5 model, with the latter two methodsrequiring costly data collection, fine-tuning and post-processing. However, thetask of prompting large language models to specialize in specific textprediction tasks can be challenging, particularly for designers withoutexpertise in prompt engineering. To address this, we introduce Promptor, aconversational prompt generation agent designed to engage proactively withdesigners. Promptor can automatically generate complex prompts tailored to meetspecific needs, thus offering a solution to this challenge. We conducted a userstudy involving 24 participants creating prompts for three intelligent textentry tasks, half of the participants used Promptor while the other halfdesigned prompts themselves. The results show that Promptor-designed promptsresult in a 35% increase in similarity and 22% in coherence over those bydesigners.

Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques