Abstract
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 *smooth control* of LLM generation. Specifically, we propose metrics to assess the range, calibration, and consistency