Promptor：面向智能文本输入技术的对话式自主提示生成智能体
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques
AI总结：
本文提出Promptor，一个对话式自主提示生成智能体，通过自动生成复杂提示，使大型语言模型无需微调即可胜任智能文本输入任务，实验显示其提示在相似性和连贯性上分别提升35%和22%。
摘要：
文本输入是我们日常数字交互中的一项基本任务。为简化这一过程，已开发出众多智能功能，使文本输入更加有效、高效和流畅。这些改进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主流，数据收集和模型微调的需求也在增加。这些挑战可以通过利用GPT-3.5等大型语言模型的上下文学习能力来缓解。这一独特特性使语言模型能够通过提示获取新技能，无需数据收集和微调。因此，大型语言模型可以学习各种文本预测技术。我们最初的研究表明，在句子预测任务中，仅通过提示GPT-3.5就超越了基于GPT-2的系统，并与微调后的GPT-3.5模型相当，而后两种方法需要昂贵的数据收集、微调和后处理。然而，提示大型语言模型以专精于特定文本预测任务可能具有挑战性，尤其是对于缺乏提示工程专业知识的设计师而言。为解决这一问题，我们引入了Promptor，一个对话式提示生成智能体，旨在与设计师主动互动。Promptor能够自动生成满足特定需求的复杂提示，从而为这一挑战提供了解决方案。我们进行了一项用户研究，涉及24名参与者为三个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。结果表明，与设计师自行设计的提示相比，Promptor设计的提示在相似性上提高了35%，在连贯性上提高了22%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
