We study whether automatically-induced prompts that effectively extract
information from a language model can also be used, out-of-the-box, to probe
other language models for the same information. After confirming that discrete
prompts induced with the AutoPrompt algorithm outperform manual and semi-manual
prompts on the slot-filling task, we demonstrate a drop in performance for
AutoPrompt prompts learned on a model and tested on another. We introduce a way
to induce prompts by mixing language models at training time that results in
prompts that generalize well across models. We conduct an extensive analysis of
the induced prompts, finding that the more general prompts include a larger
proportion of existing English words and have a less order-dependent and more
uniform distribution of information across their component tokens. Our work
provides preliminary evidence that it's possible to generate discrete prompts
that can be induced once and used with a number of different models, and gives
insights on the properties characterizing such prompts.

该论文研究了利用自动诱导的提示从语言模型中提取信息的能力是否可以被直接应用于探索其他语言模型。在证实了自动提示优于手动和半手动提示的插槽填充任务后，我们证明了在一个模型上学习并在另一个模型上进行测试的 AutoPrompt 提示效果下降。我们介绍了一种混合语言模型来诱导提示的方法，以获得可以在多个模型之间广义的提示。我们对诱导提示进行了全面分析，发现更通用的提示包括更大比例的现有英语单词，其组成部分信息的分布更少依赖顺序并且更加均匀。我们的研究提供了初步证据表明可能生成可诱导一次并与许多不同模型一起使用的离散提示，并提供关于表征此类提示的属性的见解。