Towards Goal-oriented Large Language Model Prompting: A Survey

• DOI: 10.48550/arxiv.2401.14043
• Publication Date: 2024-01-01
• Authors: Haochen Li; Jonathan Leung; Zhiqi Shen
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL)

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