Towards Goal-oriented Large Language Model Prompting: A Survey
Haochen Li Jonathan Leung Zhiqi Shen
Large Language Models (LLMs) have shown prominent performance in various
downstream tasks in which prompt engineering plays a pivotal role in optimizing
LLMs' performance. This paper, not as an overview of current prompt engineering
methods, aims to highlight the limitation of designing prompts while holding an
anthropomorphic assumption that expects LLMs to think like humans. From our
review of 35 representative studies, we demonstrate that a goal-oriented prompt
formulation, which guides LLMs to follow established human logical thinking,
significantly improves the performance of LLMs. Furthermore, We introduce a
novel taxonomy that categorizes goal-oriented prompting methods into five
interconnected stages and we demonstrate the broad applicability of our
framework by summarizing ten applicable tasks. With four future directions
proposed, we hope to further emphasize and promote goal-oriented prompt
engineering.