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.
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.
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.