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.

翻译：随着大语言模型的广泛应用，参数高效微调等可控生成技术日益受到关注，用于定制模型并管理其输出。然而，如何精确控制提示对模型的影响仍是亟待深入研究的领域。为此，我们提出了ControlPE（连续可控提示工程）。ControlPE能够对提示效果进行更精细的调整，作为现有提示工程的补充，并有效控制连续目标。该方法利用LoRA（低秩适应）技术实现类似提示加权的效果，从而对提示的影响进行微调。我们的工作包括生成用于提示蒸馏的专用数据集、将这些提示融入LoRA模型，并精心调整LoRA合并权重以调控提示的影响力，从而为提示控制提供动态且可适应的工具。通过实验，我们验证了ControlPE的实用性和有效性，证明其在控制多种提示（从生成简短回复的提示、拒绝提示到思维链提示）方面具有广阔前景。