mlsanityData to 7 Oct 2026

Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey

arXiv 2401.14043 ↗First posted 25 Jan 2024cs.CL cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 63 of its 76 references that are in the map. First posted on arXiv 25 Jan 2024.

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63 of 76 references.

References · 63

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

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