Helping Language Models Learn More: Multi-dimensional Task Prompt for
  Few-shot Tuning
Jinta Weng Jiarui Zhang Yue Hu Daidong Fa Xiaofeng Xuand Heyan Huang
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.