对话式提示工程
Conversational Prompt Engineering
AI总结：
本文提出对话式提示工程（CPE），通过聊天模型与用户简短交互，利用未标注数据和用户反馈逐步生成个性化少样本提示，实验表明其零样本提示效果可与更长的少样本提示相媲美，显著节省重复性任务成本。
摘要：
提示是人类与大型语言模型（LLMs）进行交流的方式。信息丰富的提示对于引导 LLMs 产生期望的输出至关重要。然而，提示工程通常繁琐且耗时，需要大量专业知识，这限制了它的广泛使用。我们提出了对话式提示工程（CPE），这是一个用户友好的工具，可帮助用户为其特定任务创建个性化提示。CPE 使用聊天模型与用户进行简短交互，帮助他们明确输出偏好，并将这些偏好整合到提示中。该过程包括两个主要阶段：首先，模型使用用户提供的未标注数据生成数据驱动的问题，并利用用户的回答来塑造初始指令。然后，模型分享由该指令生成的输出，并利用用户反馈进一步细化指令和输出。最终结果是一个少样本提示，其中经用户认可的输出作为少样本示例。一项关于摘要任务的用户研究证明了 CPE 在创建个性化、高性能提示方面的价值。结果表明，所获得的零样本提示与其篇幅长得多的少样本对应物相当，这表明在涉及大量文本的重复性任务场景中可以显著节省成本。
英文摘要：
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and time-consuming, requiring significant expertise, limiting its widespread use. We propose Conversational Prompt Engineering (CPE), a user-friendly tool that helps users create personalized prompts for their specific tasks. CPE uses a chat model to briefly interact with users, helping them articulate their output preferences and integrating these into the prompt. The process includes two main stages: first, the model uses user-provided unlabeled data to generate data-driven questions and utilize user responses to shape the initial instruction. Then, the model shares the outputs generated by the instruction and uses user feedback to further refine the instruction and the outputs. The final result is a few-shot prompt, where the outputs approved by the user serve as few-shot examples. A user study on summarization tasks demonstrates the value of CPE in creating personalized, high-performing prompts. The results suggest that the zero-shot prompt obtained is comparable to its - much longer - few-shot counterpart, indicating significant savings in scenarios involving repetitive tasks with large text volumes.
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。
