Large language models (LLMs) can be used as accessible and intelligent
chatbots by constructing natural language queries and directly inputting the
prompt into the large language model. However, different prompt' constructions
often lead to uncertainty in the answers and thus make it hard to utilize the
specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an
interpretable structure to explain the prompt learning principle in LLMs, which
certificates that the effectiveness of language models is determined by
position changes of the task's related tokens. Therefore, we propose MTPrompt,
a multi-dimensional task prompt learning method consisting based on
task-related object, summary, and task description information. By
automatically building and searching for appropriate prompts, our proposed
MTPrompt achieves the best results on few-shot samples setting and five
different datasets. In addition, we demonstrate the effectiveness and stability
of our method in different experimental settings and ablation experiments. In
interaction with large language models, embedding more task-related information
into prompts will make it easier to stimulate knowledge embedded in large
language models.

通过提出多维任务提示学习方法（MTPrompt），将更多的任务相关信息嵌入提示中，以激发大型语言模型中嵌入的知识，从而实现在少样本和五个不同数据集上取得最佳结果，并在不同实验设置和削减实验中展示了该方法的有效性和稳定性。