iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop

2024-12-17

arXiv.org

AI Summary

Prompt engineering heavily relies on manual expertise, while automated optimization methods suffer from a lack of labeled data. Method: This paper proposes a human-in-the-loop interactive prompt optimization framework that uniquely integrates real-time human judgment into the optimization loop. It combines active learning sampling, LLM-generated self-explanations, lightweight performance evaluation, and an interactive visualization interface—enabling domain experts (without programming skills) to iteratively refine prompts based on model explanations, sample-level feedback, and metric analysis. Contribution/Results: Experiments demonstrate significant improvements in prompt quality across diverse tasks. The framework enables non-technical users to efficiently construct high-performance, task-specific prompts. Furthermore, it uncovers critical intrinsic factors governing prompt optimization efficacy—namely semantic consistency, sample representativeness, and explanation credibility—thereby advancing both practical prompt engineering and foundational understanding of LLM behavior.

Technology Category

Natural Language Processing: Prompt Engineering / PromptingSearch and Optimization: Learning to SearchHumans and AI: Human-in-the-loop Machine Learning

Application Category

Economics, Online Markets and Human Computation: LLM based quality controls for crowd workSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Prompt engineering has made significant contributions to the era of large language models, yet its effectiveness depends on the skills of a prompt author. Automatic prompt optimization can support the prompt development process, but requires annotated data. This paper introduces $ extit{iPrOp}$, a novel Interactive Prompt Optimization system, to bridge manual prompt engineering and automatic prompt optimization. With human intervention in the optimization loop, $ extit{iPrOp}$ offers users the flexibility to assess evolving prompts. We present users with prompt variations, selected instances, large language model predictions accompanied by corresponding explanations, and performance metrics derived from a subset of the training data. This approach empowers users to choose and further refine the provided prompts based on their individual preferences and needs. This system not only assists non-technical domain experts in generating optimal prompts tailored to their specific tasks or domains, but also enables to study the intrinsic parameters that influence the performance of prompt optimization. Our evaluation shows that our system has the capability to generate improved prompts, leading to enhanced task performance.

Problem

• Bridging manual and automatic prompt optimization for LLMs
• Enhancing human engagement in interactive prompt refinement
• Assisting non-technical users in generating task-specific prompts

Innovation

• Interactive prompt optimization with human feedback
• Task-specific guidance using LLM predictions and metrics
• Flexible prompt refinement based on user preferences