PAS：高效且節省資料的即插即用提示增強系統

摘要

近年來，大型語言模型（LLMs）的崛起推動了對即插即用人工智慧系統的不斷增長需求。在各種人工智慧技術中，提示工程顯得特別重要。然而，由於陡峭的學習曲線和需投入大量時間，使用者常常在撰寫提示時遇到困難，現有的自動提示工程（APE）模型也可能難以應用。為了解決這個問題，我們提出了PAS，一個基於LLM的即插即用提示工程系統。PAS利用在高質量自動生成的提示互補數據集上訓練的LLMs，表現出色。在全面的基準測試中，PAS相較於先前的APE模型達到了最先進的結果，平均提升了6.09個點。此外，PAS極具效率，僅需9000個數據點即可實現最先進的性能。此外，PAS能夠自主生成提示擴充數據，無需額外的人力。其靈活性也使其與所有現有的LLMs兼容，適用於各種任務。PAS在人類評估中表現優異，突顯其作為用戶插件的適用性。PAS結合了高性能、高效率和靈活性，使其成為一個有價值的系統，通過改進提示工程來增強LLMs的可用性和效力。

English

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：高效且節省資料的即插即用提示增強系統

PAS: Data-Efficient Plug-and-Play Prompt Augmentation System

摘要

Summary

Support

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