title: PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
summary: In recent years, the rise of Large Language Models (LLMs) has spurred a
  growing demand for plug-and-play AI systems. Among the various AI techniques,
  prompt engineering stands out as particularly significant. However, users often
  face challenges in writing prompts due to the steep learning curve and
  significant time investment, and existing automatic prompt engineering (APE)
  models can be difficult to use. To address this issue, we propose PAS, an
  LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality,
  automatically generated prompt complementary datasets, resulting in exceptional
  performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA)
  results compared to previous APE models, with an average improvement of 6.09
  points. Moreover, PAS is highly efficient, achieving SoTA performance with only
  9000 data points. Additionally, PAS can autonomously generate prompt
  augmentation data without requiring additional human labor. Its flexibility
  also allows it to be compatible with all existing LLMs and applicable to a wide
  range of tasks. PAS excels in human evaluations, underscoring its suitability
  as a plug-in for users. This combination of high performance, efficiency, and
  flexibility makes PAS a valuable system for enhancing the usability and
  effectiveness of LLMs through improved prompt engineering.
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paper_authors.name: Miao Zheng
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paper_authors.name: Hao Liang
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paper_authors.name: Fan Yang
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paper_authors.fullname: Alvin Sun
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paper_authors.name: Tianpeng Li
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paper_authors.name: Lingchu Xiong
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paper_authors.name: Yan Zhang
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paper_authors.name: Yozhen Wu
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paper_authors.name: Kun Li
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paper_authors.name: Yanjun Sheng
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paper_authors.name: Mingan Lin
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paper_authors.fullname: lma
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paper_authors.user: lin5547
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paper_authors.name: Tao Zhang
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paper_authors.name: Guosheng Dong
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paper_authors.fullname: guosheng dong
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paper_authors.user: dongguosheng
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paper_authors.name: Yujing Qiao
paper_authors._id: 668d0d05781c9e63c563d47c
paper_authors.name: Kun Fang
paper_authors._id: 668d0d05781c9e63c563d47d
paper_authors.name: Weipeng Chen
paper_authors._id: 668d0d05781c9e63c563d47e
paper_authors.name: Bin Cui
paper_authors._id: 668d0d05781c9e63c563d47f
paper_authors.name: Wentao Zhang
paper_authors._id: 668d0d05781c9e63c563d480
paper_authors.name: Zenan Zhou
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paper_authors.fullname: Zenan Zhou
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paper_title: PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
paper_summary: In recent years, the rise of Large Language Models (LLMs) has spurred a
  growing demand for plug-and-play AI systems. Among the various AI techniques,
  prompt engineering stands out as particularly significant. However, users often
  face challenges in writing prompts due to the steep learning curve and
  significant time investment, and existing automatic prompt engineering (APE)
  models can be difficult to use. To address this issue, we propose PAS, an
  LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality,
  automatically generated prompt complementary datasets, resulting in exceptional
  performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA)
  results compared to previous APE models, with an average improvement of 6.09
  points. Moreover, PAS is highly efficient, achieving SoTA performance with only
  9000 data points. Additionally, PAS can autonomously generate prompt
  augmentation data without requiring additional human labor. Its flexibility
  also allows it to be compatible with all existing LLMs and applicable to a wide
  range of tasks. PAS excels in human evaluations, underscoring its suitability
  as a plug-in for users. This combination of high performance, efficiency, and
  flexibility makes PAS a valuable system for enhancing the usability and
  effectiveness of LLMs through improved prompt engineering.
paper_ai_keywords: Large Language Models (LLMs) plug-and-play AI systems prompt engineering automatic prompt engineering (APE) PAS (LLM-based plug-and-play APE system) high-quality, automatically generated prompt complementary datasets state-of-the-art (SoTA) results prompt augmentation data human evaluations