We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL but also an extensible vocabulary of imaginary words. Registering new imaginary words allows us to instruct the LLM to comprehend concepts that are difficult to describe with NL words, thereby making a prompt more descriptive. Also, these imaginary words are designed to be out-of-distribution (OOD) robust so that they can be (re)used like NL words in various prompts, distinguishing X-Prompt from soft prompt that is for fitting in-distribution data. We propose context-augmented learning (CAL) to learn imaginary words for general usability, enabling them to work properly in OOD (unseen) prompts. We experiment X-Prompt for zero-shot language style customization as a case study. The promising results of X-Prompt demonstrate its potential to facilitate advanced interaction beyond the natural language interface, bridging the communication gap between humans and LLMs.

翻译：我们提出可扩展提示词（X-Prompt），以超越自然语言的方式对大语言模型进行提示。X-Prompt不仅使用自然语言，还通过可扩展的虚构词汇表对LLM进行指令引导。注册新的虚构词汇使我们能够指示LLM理解难以用自然语言词汇描述的概念，从而使提示词更具描述性。此外，这些虚构词汇被设计为具有分布外鲁棒性，可像自然语言词汇一样在各种提示词中（重复）使用，这使X-Prompt区别于仅用于拟合分布内数据的软提示。我们提出上下文增强学习来学习具有通用可用性的虚构词汇，使其能在未见过的分布外提示词中正常工作。作为案例研究，我们将X-Prompt应用于零样本语言风格定制。X-Prompt的显著结果证明了其在自然语言界面之外促进高级交互的潜力，从而弥合人类与LLM之间的沟通鸿沟。