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

Area of Science:

• Natural Language Processing (NLP)
• Human-Computer Interaction (HCI)
• Visual Analytics

Background:

• Large language models (LLMs) are popular for ad-hoc NLP tasks via natural language prompts.
• Prompt variations significantly impact LLM task performance.
• Non-experts struggle to optimize prompts without domain knowledge or feedback.

Purpose of the Study:

• Introduce PROMPTAID, a visual analytics system for interactive prompt creation and refinement.
• Enable users to explore, perturb, test, and iterate on prompts.
• Address the challenge of prompt optimization for non-expert users.

Main Methods:

• PROMPTAID employs coordinated visualizations for prompt improvement.
• Utilizes strategies like keyword and paraphrasing perturbations.
• Incorporates obtaining optimal in-context few-shot examples.
• Design involved NLP expert pre-study and mixed-methods user evaluation.

Main Results:

• PROMPTAID reduces cognitive overhead during prompt iteration.
• Facilitates generation of diverse prompts through recommendations.
• Enables effective analysis of prompt performance.
• Outperforms existing state-of-the-art prompting interfaces.

Conclusions:

• PROMPTAID empowers users, especially non-experts, to effectively create and refine LLM prompts.
• Visual analytics can significantly enhance the usability and performance of prompt engineering.
• The system offers a novel approach to interactive prompt optimization for NLP tasks.