Controlling the attribute intensity of text generation is crucial across
scenarios (e.g., writing conciseness, chatting emotion, and explanation
clarity). The remarkable capabilities of large language models (LLMs) have
revolutionized text generation, prompting us to explore such \emph{smooth
control} of LLM generation. Specifically, we propose metrics to assess the
range, calibration, and consistency of the generated text's attribute intensity
in response to varying control values, as well as its relevance to the intended
context. To quantify the attribute intensity and context relevance, we propose
an effective evaluation framework leveraging the Elo rating system and GPT4,
both renowned for their robust alignment with human judgment. We look into two
viable training-free methods for achieving smooth control of LLMs: (1)
Prompting with semantic shifters, and (2) Modifying internal model
representations. The evaluations of these two methods are conducted on $5$
different attributes with various models. Our code and dataset can be obtained
from https://github.com/ShangDataLab/Smooth-Control.

大语言模型（LLM）的平滑控制在文本生成中具有重要意义，本论文提出了评估生成文本属性强度范围、校准度和一致性以及其与预期语境的相关性的度量指标，通过引入 Elo 评级系统和 GPT4 的评估方法来量化属性强度和上下文相关性，并研究了两种无需训练的实现语言模型平滑控制的方法：使用语义转换器进行提示和修改内部模型表示。