PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
Project Overview
The document explores the use of generative AI in education through the introduction of PAS, a plug-and-play system designed for automatic prompt augmentation in large language models (LLMs). It emphasizes the importance of effective prompt engineering to enhance LLM performance while minimizing the need for extensive human intervention. PAS showcases its capabilities by achieving state-of-the-art results with just 9,000 data points, illustrating its efficiency and adaptability across various educational applications. The findings suggest that PAS can significantly improve the interaction between students and AI systems, facilitating personalized learning experiences and better resource accessibility. Overall, the implementation of PAS in educational settings highlights the transformative potential of generative AI, offering streamlined solutions for content generation, tutoring, and assessment, ultimately aiming to enhance learning outcomes and support educators in their teaching endeavors.
Key Applications
PAS (Prompt Augmentation System)
Context: Applicable to various educational contexts involving LLMs for enhancing prompt quality.
Implementation: PAS uses a dataset of high-quality prompts to fine-tune LLMs, enhancing their performance through automatic prompt augmentation.
Outcomes: Improved model performance, achieving an average improvement of 8 points over models without PAS, and 6.09 points over the previous state-of-the-art model.
Challenges: Initial human labor required for dataset creation but minimized in ongoing usage.
Implementation Barriers
Data Dependency
Requires high-quality prompt datasets for effective training.
Proposed Solutions: Utilizes automated methods for dataset generation to reduce reliance on human labor.
User Experience
Prompt engineering can be complex and user-unfriendly for non-technical users.
Proposed Solutions: PAS aims to simplify the process by providing intuitive prompt enhancements.
Project Team
Miao Zheng
Researcher
Hao Liang
Researcher
Fan Yang
Researcher
Haoze Sun
Researcher
Tianpeng Li
Researcher
Lingchu Xiong
Researcher
Yan Zhang
Researcher
Youzhen Wu
Researcher
Kun Li
Researcher
Yanjun Shen
Researcher
Mingan Lin
Researcher
Tao Zhang
Researcher
Guosheng Dong
Researcher
Yujing Qiao
Researcher
Kun Fang
Researcher
Weipeng Chen
Researcher
Bin Cui
Researcher
Wentao Zhang
Researcher
Zenan Zhou
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Miao Zheng, Hao Liang, Fan Yang, Haoze Sun, Tianpeng Li, Lingchu Xiong, Yan Zhang, Youzhen Wu, Kun Li, Yanjun Shen, Mingan Lin, Tao Zhang, Guosheng Dong, Yujing Qiao, Kun Fang, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai