Key Points

1. The paper introduces Conversational Prompt Engineering (CPE), an alternative approach to prompt engineering that removes the need for labeled data and initial prompts.

2. CPE is based on three core insights: 1) Chat models can assist users in better understanding and articulating their exact task requirements, 2) Unlabeled input texts can be leveraged by language models to suggest data-specific dimensions of potential output preferences, and 3) User feedback on specific model-generated outputs can be used to refine the instruction.

3. CPE is designed for users who need to perform the same task repeatedly on large volumes of text, such as in enterprise scenarios like email summarization or content generation.

4. The CPE process involves: 1) Initialization where the user selects the target model and provides unlabeled data, 2) Initial discussion and first instruction creation where the model analyzes the data and suggests an initial instruction, 3) Iterative instruction refinement based on user feedback, 4) Output generation and user evaluation, and 5) Final prompt generation.

5. CPE uses a three-party architecture with a user, a system, and a language model. The system orchestrates the interaction, while the model handles the core capabilities like data analysis, instruction generation, and output enhancement.

6. The paper presents the results of a user study on summarization tasks, which demonstrates the effectiveness of CPE in creating personalized, high-performing prompts. The zero-shot prompt obtained is comparable to the much longer few-shot counterpart, indicating significant savings in repetitive task scenarios.

7. Analysis of the user interactions shows that in two-thirds of the cases, the final instruction was refined through multiple iterations, indicating the value of the output enhancement phase.

8. Comparing the initial and final CPE instructions reveals substantial differences, underscoring the potential of CPE to refine instructions and achieve a detailed resolution of task requirements.

9. The paper concludes by discussing potential future work, such as integrating CPE with existing automatic prompt engineering methods and exploring the use of CPE in agentic workflow creation.

Summary

The paper "Conversational Prompt Engineering" addresses the challenges of prompt engineering (PE) for Large Language Models (LLMs) and proposes Conversational Prompt Engineering (CPE) as an alternative approach. Prompt engineering is time-consuming and computationally demanding, requiring a deep understanding of how LLMs interpret and respond to instructions. Previous works aimed to automate PE, but they require labeled data and manually-created seed prompts. CPE aims to remove the need for labeled data and initial prompts by utilizing chat models to assist users in creating prompts through a brief and user-friendly conversation, considering a small set of unlabeled data provided by the user. CPE is based on three core insights: (1) chat models assist users in understanding and articulating their exact requirements, (2) unlabeled input texts can be leveraged by LLMs to suggest data-specific dimensions of potential output preferences, and (3) user feedback on specific model-generated outputs can refine the instruction to be applied to unseen texts. The CPE workflow involves two main stages: the model uses user-provided unlabeled data to generate data-driven questions and utilize user responses to shape the initial instruction, and then the model shares the outputs generated by the instruction and uses user feedback to further refine the instruction and the outputs.

Description of CPE Implementation and User Study Results
The paper describes the CPE implementation, including its user interface, the target model, instruction, and prompt generation stages. It also outlines the critical components and their realization within CPE, such as planning, reflection, and the agentic workflows. The paper presents the results of a user study on summarization tasks, demonstrating the value of CPE in creating personalized, high-performing prompts. The study showed that the CPE Zero-Shot (ZS) prompt obtained comparable results to its Few-Shot (FS) counterpart, indicating significant savings in scenarios involving repetitive tasks with large text volumes.

Analysis of User Interactions and Future Directions
The paper also provides an analysis of the interactions users had with CPE, showing that the chats required an average of 32 turns to reach convergence. The study confirmed the benefits of using CPE to create prompts that effectively meet user requirements. Additionally, the paper discusses potential future directions, such as evaluating whether CPE prompts can be further improved by using them as initial prompts for existing automatic PE methods and exploring whether the CPE approach could assist users in planning and creating agentic workflows.

In conclusion, the paper presents CPE as a user-friendly tool that helps users create personalized prompts for specific tasks and demonstrates its effectiveness in addressing the challenges of prompt engineering for LLMs.

Reference: https://arxiv.org/abs/2408.04560