PromptAid, a visual analytics system, empowers non-expert users to interactively create, refine, and test prompts for large language models, leading to improved task performance through exploration, perturbation, and iteration.
Abstract
Large language models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc natural language processing (NLP) tasks with simple natural language prompts. Part of the appeal for LLMs is their approachability to the general public, including individuals with little technical expertise in NLP. However, prompts can vary significantly in terms of their linguistic structure, context, and other semantics, and 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 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 coordinated visualizations which allow users to improve prompts via three strategies: keyword perturbations, paraphrasing perturbations, and obtaining the best set of in-context few-shot examples. PromptAid was designed through a pre-study involving NLP experts, and evaluated via a robust mixed-methods user study. Our findings indicate that PromptAid helps users to iterate over prompts with less cognitive overhead, generate diverse prompts with the help of recommendations, and analyze the performance of the generated prompts while surpassing existing state-of-the-art prompting interfaces in performance.