Abstract

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly perfect prompt for a task. To mitigate the high degree of effort involved in prompting, we instead ask whether collecting multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, Ask Me Anything Prompting (AMA). We first develop an understanding of the effective prompt formats, finding question-answering (QA) prompts, which encourage open-ended generation (“Who went to the park?”) tend to outperform those that restrict the model outputs (“John went to the park. Output True or False”). Our approach recursively uses the LLM to transform task inputs to the effective QA format. We apply these prompts to collect several noisy votes for the input’s true label. We find that these prompts can have very