To be or not to be? an exploration of continuously controllable prompt
  engineering
Yuhan Sun Mukai Li Yixin Cao Kun Wang Wenxiao Wang Xingyu Zeng Rui Zhao
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.