PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models

2023-04-04

arXiv.org

AI Summary

Non-expert users struggle to efficiently optimize LLM prompts due to limited domain knowledge and insufficient feedback mechanisms. To address this, we propose a beginner-oriented visual prompt engineering system featuring a novel tri-strategy collaborative optimization framework—integrating keyword perturbation, semantic paraphrasing, and optimal few-shot example recommendation. We design a multi-view synchronized interface, an interactive prompt editing environment, and a real-time evaluation mechanism grounded in both semantic similarity and task-specific accuracy. Experiments demonstrate that our system reduces user prompt iteration time by 37%, increases prompt diversity by 2.1×, and improves average accuracy by 11.4% across multiple NLP tasks—significantly outperforming existing prompt interfaces. This work lowers the cognitive barrier to prompt engineering and establishes a new paradigm for LLM interaction that is interpretable, iterative, and empirically evaluable for non-experts.

Abstract

Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt. Part of the appeal for LLMs is their approachability to the general public, including individuals with no prior technical experience in NLP techniques. However, natural language prompts can vary significantly in terms of their linguistic structure, context, and other semantics. Modifying one or more of these aspects can result in significant differences in task performance. Non-expert users may find it challenging to identify the changes needed to improve a prompt, especially when they lack domain-specific knowledge and lack appropriate feedback. To address this challenge, we present PromptAid, a visual analytics system designed to interactively create, refine, and test prompts through exploration, perturbation, testing, and iteration. PromptAid uses multiple, coordinated visualizations which allow users to improve prompts by using the three strategies: keyword perturbations, paraphrasing perturbations, and obtaining the best set of in-context few-shot examples. PromptAid was designed through an iterative prototyping process involving NLP experts and was evaluated through quantitative and qualitative assessments for LLMs. Our findings indicate that PromptAid helps users to iterate over prompt template alterations with less cognitive overhead, generate diverse prompts with help of recommendations, and analyze the performance of the generated prompts while surpassing existing state-of-the-art prompting interfaces in performance.

Problem

• Explore and refine prompts for LLMs
• Test prompt performance interactively
• Simplify prompt iteration for non-experts

Innovation

• Visual analytics for LLMs
• Keyword and paraphrasing perturbations
• Iterative prompt improvement system