text: 'behavior. Such prompts are typically hand engineered, but can '                 'also be learned with gradient-based methods from labeled data. '                 'However, it is underexplored what factors make the prompts '                 'effective, especially when the prompts are natural language. In '                 'this paper, we investigate common attributes shared by effective '                 'prompts. We first propose a human readable prompt tuning method '                 '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '                 'fluency constraint to find a diverse distribution of effective '                 'and fluent prompts. Our analysis reveals that effective prompts '                 'are topically related to the task domain and calibrate the prior '                 'probability of label words. Based on these findings, we also '                 'propose a method for generating prompts using only unlabeled '                 'data, outperforming strong baselines by an average of 7.0% '