As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for customizing models and managing their outputs. However, the challenge of precisely controlling how prompts influence these models is an area ripe for further investigation. In response, we introduce ControlPE (Continuously Controllable Prompt Engineering). ControlPE enables finer adjustments to prompt effects, complementing existing prompt engineering, and effectively controls continuous targets. This approach harnesses the power of LoRA (Low-Rank Adaptation) to create an effect akin to prompt weighting, enabling fine-tuned adjustments to the impact of prompts. Our methodology involves generating specialized datasets for prompt distillation, incorporating these prompts into the LoRA model, and carefully adjusting LoRA merging weight to regulate the influence of prompts. This provides a dynamic and adaptable tool for prompt control. Through our experiments, we have validated the practicality and efficacy of ControlPE. It proves to be a promising solution for control a variety of prompts, ranging from generating short responses prompts, refusal prompts to chain-of-thought prompts.

ControlPE是一种对大型语言模型进行微调和精细控制的技术，利用LoRA实现连续的Prompt引导权重调整，有效地管控生成过程中的Prompt影响。通过生成专门的数据集进行Prompt精炼，并将其应用于LoRA模型，通过调整LoRA合并权重来调控Prompt的影响，从而实现了动态且可适应的Prompt控制。通过实验证明了ControlPE在控制各种类型的Prompt上的实用性和有效性，包括生成短回答Prompt、拒绝Prompt和思路链Prompt。

是否为？对持续可控激励工程的探索