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。