Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
This is an incremental survey that provides a taxonomy and framework for researchers and practitioners to enhance LLM performance across various fields.
The paper surveys prompt engineering for large language models, highlighting that goal-oriented prompts based on human logical thinking significantly improve performance, as demonstrated in a review of 50 studies.
Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 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. With four future directions proposed, we hope to further emphasize the power and potential of goal-oriented prompt engineering in all fields.