Conversational Prompt Engineering (2408.04560v1)

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.

Conversational Prompt Engineering

The paper "Conversational Prompt Engineering" by Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, and Noam Slonim introduces a method termed Conversational Prompt Engineering (CPE). This method aims to simplify the creation of effective prompts for LLMs through a user-friendly chat interface, eliminating the need for labeled data and initial prompt seeds, which are often prerequisites for automatic prompt engineering methods.

Introduction and Motivation

At its core, the paper addresses the complexities associated with prompt engineering (PE) for LLMs. These complexities include the nuanced understanding required to craft prompts that yield high-quality outputs, and the iterative, time-consuming process typically involved. Existing automatic PE methods require labeled data and initial prompt seeds, which may not always be available or straightforward to generate.

Approach: Conversational Prompt Engineering

Conversational Prompt Engineering (CPE) operates through a chat-based model that interacts with users to generate tailored prompts suitable for specific tasks. The process consists of two primary stages:

1. Data-Driven Question Generation and Instruction Shaping:
   • The model uses user-provided unlabeled data to generate data-specific questions.
   • User responses help in shaping the initial instructions.
2. Instruction Refinement via Feedback:
   • The model generates outputs based on the initial instructions.
   • User feedback on these outputs is used to further refine the instruction.

The final result is a few-shot prompt incorporating user-approved outputs as examples. Notably, the use of zero-shot prompts in the paper demonstrated effectiveness comparable to few-shot counterparts, signifying potential savings in tasks involving repetitive processing of large text volumes.

System Workflow

The CPE workflow can be broken down into the following stages:

1. Initialization:
   • Users select their target model and upload a set of unlabeled data.
2. Initial Discussion and Prompt Creation:
   • Interaction with the chat model to discuss data-specific output preferences and generate initial instructions.
3. Instruction Refinement:
   • Refinement of instructions based on user feedback regarding the proposed prompt outputs.
4. Output Generation:
   • The target model generates outputs using the refined prompt.
5. User Feedback and Output Refinement:
   • Iterative enhancement based on user feedback until satisfactory outputs are achieved.
6. Final Few-Shot Prompt Generation:
   • Conclusion of the chat with the generation of a few-shot prompt incorporating user feedback.

User Study and Evaluation

A user paper involving 12 participants was conducted to evaluate the effectiveness of CPE in summarization tasks. Key results include:

• The user paper's survey data indicated high satisfaction with final instructions, the conversational process, and overall chat pleasantness. Convergence time was slightly lower-rated, with an average of 25 minutes required to finalize a prompt.
• Evaluation of summary quality showed a preference for CPE-generated prompts over a baseline generic prompt. Specifically, the generated CPE zero-shot and few-shot prompts were ranked as the best in 53% and 47% of instances, respectively. This suggests that CPE's ability to integrate user preferences effectively reduces the necessity for extensive few-shot examples.

Implications and Future Directions

The practical implications of CPE are significant for enterprises and individual users who engage with repetitive tasks across large text datasets. CPE's ability to create efficient and high-performing prompts without the need for extensive labeled data or initial seed prompts can streamline workflows, reduce computational demands, and enhance the productivity of AI systems in varied applications, including text summarization and creative content generation.

From a theoretical perspective, the method highlights the potential for integrating advanced chat models to facilitate user interaction and collaboration in LLM prompt engineering. Future developments in AI could explore:

• The application of CPE-generated prompts as initial seeds for advanced automatic PE methods.
• Extending CPE techniques to aid in planning and executing complex LLM-empowered agentic workflows.

In conclusion, "Conversational Prompt Engineering" presents a robust, user-centric approach to simplifying the prompt engineering process for LLMs. The method’s effectiveness in reducing the dependency on labeled data and initial seeds while producing satisfactory outputs has strong implications for practical AI implementations in various domains.