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Large language models can perform new tasks in a zero-shot fashion, givennatural language prompts that specify the desired behavior. Such prompts aretypically hand engineered, but can also be learned with gradient-based methodsfrom labeled data. However, it is underexplored what factors make the promptseffective, especially when the prompts are natural language. In this paper, weinvestigate common attributes shared by effective prompts. We first propose ahuman readable prompt tuning method (F LUENT P ROMPT) based on Langevindynamics that incorporates a fluency constraint to find a diverse distributionof effective and fluent prompts. Our analysis reveals that effective promptsare topically related to the task domain and calibrate the prior probability oflabel words. Based on these findings, we also propose a method for generatingprompts using only unlabeled data, outperforming strong baselines by an averageof 7.0% accuracy across three tasks.

Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?