With the continuous accumulation of application and practical experience of Large Language Models (LLMs), the methodology of "how to write prompts" has gradually formed a new set of theories.
The PAS system, through automated prompt supplementation, has significantly improved the performance of LLMs, achieving an improvement of more than 6 percentage points compared to the previously state-of-the-art model, BPO.
Moreover, the achievement of PAS was accomplished with less than 65% of the fine-tuning data volume used by BPO, further demonstrating the advantage of PAS in data efficiency, and providing a strong guiding direction for the research and expansion of APE .
With the continuous accumulation of application and practical experience of Large Language Models (LLMs), the methodology of "how to write prompts" has gradually formed a new set of theories.
The PAS system, through automated prompt supplementation, has significantly improved the performance of LLMs, achieving an improvement of more than 6 percentage points compared to the previously state-of-the-art model, BPO.
Moreover, the achievement of PAS was accomplished with less than 65% of the fine-tuning data volume used by BPO, further demonstrating the advantage of PAS in data efficiency, and providing a strong guiding direction for the research and expansion of APE .
Higher performance at lower cost