PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
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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Miao Zheng
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Hao Liang
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Fan Yang
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FanYang
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Haoze Sun
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Alvin Sun
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AlvinSunYooo
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Tianpeng Li
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李天鹏
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yuanshuai
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Lingchu Xiong
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Yan Zhang
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Yozhen Wu
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Kun Li
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Yanjun Sheng
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Mingan Lin
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lma
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lin5547
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Tao Zhang
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Guosheng Dong
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guosheng dong
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dongguosheng
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Yujing Qiao
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Kun Fang
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Bin Cui
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Wentao Zhang
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Zenan Zhou
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Zenan Zhou
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Zenan11
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
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.
PAS is an LLM-based system for automatic prompt engineering that achieves state-of-the-art results with minimal data and human intervention, enhancing LLM usability.
Large Language Models (LLMs) prompt engineering automatic prompt engineering (APE) prompt complementary datasets human evaluations