Conversational Prompt Engineering

Original Paper: https://arxiv.org/abs/2408.04560

By: Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim

Abstract:

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.

Summary Notes

Introduction

In the world of Large Language Models (LLMs), the art of crafting effective prompts—known as prompt engineering (PE)—is crucial but often tedious and time-consuming.

A recent research paper by IBM Research introduces a novel approach called Conversational Prompt Engineering (CPE) that aims to simplify this process.

This blog post will delve into how CPE works, its methodologies, key findings, and potential applications.

The Challenge of Prompt Engineering

Prompt engineering involves creating precise instructions for LLMs to generate desired outputs. This task demands significant expertise and is computationally intensive, limiting its accessibility.

Traditional methods often rely on labeled data, which is hard to obtain, and require manually crafted seed prompts.

Recent automated methods, while promising, still depend on these prerequisites.

Introducing Conversational Prompt Engineering (CPE)

CPE is designed to alleviate the burdens of traditional PE by utilizing an interactive chat model.

It helps users articulate their preferences through a brief conversation, generating personalized prompts without the need for labeled data or initial seed prompts.

The core idea is to make PE more accessible and user-friendly, allowing even those with limited expertise to create high-quality prompts.

Methodology: How CPE Works

CPE operates in several key stages, each involving a structured interaction between the user, the system, and the model:

1. Initialization:
   • Target Model Selection: The user selects the target LLM.
   • User Data Initialization: The user uploads an unlabeled data file.
     
2. Initial Discussion and First Instruction Creation:
   • The model analyzes three examples from the user data.
   • It engages in a conversation with the user to understand their output preferences.
   • Based on this interaction, the model generates an initial instruction.
     
3. Instruction Refinement:
   • The user provides feedback on the initial instruction and initial prompt outputs.
   • The model refines the instruction based on this feedback.
     
4. Output Generation:
   • The refined instruction is used to create a prompt.
   • This prompt is fed into the target model to generate outputs.
     
5. User Feedback and Output Enhancement:
   • The user reviews the outputs and provides feedback.
   • The model uses this feedback to further refine the instruction and outputs.
     
6. Convergence on CPE FS Prompt:
   • The process iterates until the user approves the final instruction and outputs.
   • The final result is a few-shot (FS) prompt that includes user-approved examples.

Key Findings and Results

The research conducted a user study focused on summarization tasks, involving 12 participants. Here are some key findings:

• User Satisfaction: Participants reported high satisfaction with the CPE-generated prompts, rating their experience positively across various dimensions such as satisfaction from the prompt, benefit from the conversation, and overall pleasantness.
  
• Quality of Outputs: The study showed that summaries generated using CPE prompts were preferred over those generated by a baseline prompt. Interestingly, there was no significant difference between zero-shot (ZS) and few-shot (FS) CPE prompts, indicating the robustness of the initial CPE-generated instructions.
  
• Efficiency: On average, it took 32 turns and about 25 minutes for users to converge on a final prompt. While this might seem lengthy, the time investment is justified by the quality of the outputs.

Implications and Applications

CPE holds significant promise for various applications:

• Enterprise Use-Cases: Tasks such as summarizing email threads, generating personalized advertising content, and other repetitive tasks can greatly benefit from CPE. It reduces the need for extensive PE expertise and speeds up the prompt creation process.
  
• Broader Accessibility: By simplifying PE, CPE can democratize the use of LLMs, making them accessible to a wider audience, including those without specialized knowledge.
  
• Cost Efficiency: Since CPE can create effective prompts without the need for extensive labeled data or multiple examples, it can significantly reduce the computational cost associated with PE.

Limitations and Future Research

While CPE shows great potential, there are areas for improvement:

• Convergence Time: Although the study participants were generally satisfied, there is room to reduce the time required to converge on a final prompt.
  
• Extension Beyond PE: Future research could explore extending CPE to other areas, such as planning and creating agentic workflows for LLMs.

Conclusion

Conversational Prompt Engineering represents a significant advancement in making prompt engineering more accessible and efficient.

By leveraging interactive chat models, CPE helps users create high-quality, personalized prompts with minimal effort. This innovation not only enhances the usability of LLMs but also opens up new possibilities for their application across various domains.

As the field of LLMs continues to evolve, approaches like CPE will be crucial in bridging the gap between advanced technology and practical usability, ensuring that the power of these models can be harnessed by a broader audience.