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

• URL: http://arxiv.org/abs/2401.14043v3
• Date: Tue, 17 Sep 2024 04:56:03 GMT
• Title: Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey
• Authors: Haochen Li, Jonathan Leung, Zhiqi Shen,
• Abstract summary: Large Language Models (LLMs) have shown prominent performance in various downstream tasks. This paper aims to highlight the limitation of designing prompts based on an anthropomorphic assumption.
• Score: 4.362755917924305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap [7.63638387750336]
  We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE)<n>To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns to task-oriented RE roles.
  arXiv  Detail & Related papers  (2025-07-10T12:02:56Z)
• Large Language Models in Argument Mining: A Survey [15.041650203089057]
  Argument Mining (AM) focuses on extracting argumentative structures from text.<n>The advent of Large Language Models (LLMs) has profoundly transformed AM, enabling advanced in-context learning.<n>This survey systematically synthesizes recent advancements in LLM-driven AM.
  arXiv  Detail & Related papers  (2025-06-19T15:12:58Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models [9.566432486156335]
  Large Language Models for Post-Ranking in search engine (LLM4PR) We introduce a novel paradigm named Large Language Models for Post-Ranking in search engine (LLM4PR)
  arXiv  Detail & Related papers  (2024-11-02T08:36:16Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents [27.807695570974644]
  We propose a novel method, textscRePrompt, which does agradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents. By leveraging intermediate feedback, textscRePrompt can optimize the prompt without the need for a final solution checker.
  arXiv  Detail & Related papers  (2024-06-17T01:23:11Z)
• From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
  Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting. We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
  arXiv  Detail & Related papers  (2024-05-30T09:42:54Z)
• A Survey on Efficient Inference for Large Language Models [25.572035747669275]
  Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. The substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
  arXiv  Detail & Related papers  (2024-04-22T15:53:08Z)
• Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis [91.5632751731927]
  Large Language Models such as ChatGPT have showcased remarkable abilities in solving general tasks. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. We analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results.
  arXiv  Detail & Related papers  (2024-01-10T08:28:56Z)
• A Survey on Prompting Techniques in LLMs [0.0]
  Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. We identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.
  arXiv  Detail & Related papers  (2023-11-28T17:56:34Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)
• PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models [0.0]
  This paper proposes an AI-enabled methodological framework that combines the power of Large Language Models (LLMs) with the rigorous reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) By finetuning LLMs on domain-specific academic papers that have been selected as a result of a rigorous SLR process, the proposed PRISMA-DFLLM reporting guidelines offer the potential to achieve greater efficiency, reusability and scalability.
  arXiv  Detail & Related papers  (2023-06-15T02:52:50Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.