mlsanityData to 14 Oct 2026

A Survey on Prompting Techniques in LLMs

arXiv 2312.03740 ↗First posted 28 Nov 2023cs.CL cs.AIcited by 9

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Abstract (arXiv)

Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. Pre-train and prompt paradigm has replaced the conventional approach of pre-training and fine-tuning for many downstream NLP tasks. This shift has been possible largely due to LLMs and innovative prompting techniques. LLMs have shown great promise for a variety of downstream tasks owing to their vast parameters and huge datasets that they are pre-trained on. However, in order to fully realize their potential, their outputs must be guided towards the desired outcomes. Prompting, in which a specific input or instruction is provided to guide the LLMs toward the intended output, has become a tool for achieving this goal. In this paper, we discuss the various prompting techniques that have been applied to fully harness the power of LLMs. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. Further, we identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 38 of its 50 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

38 of 50 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2024A Study on Prompt Injection Attack Against LLM-Integrated Mobile Robotic Systemscited by 15
  2. 2024Urban Mobility Assessment Using LLMscited by 13
  3. 2024Evaluating the Ability of Large Language Models to Reason about Cardinal Directionscited by 10
  4. 2024The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Modelscited by 4
  5. 2025Discrete Diffusion Models for Language Generationcited by 2
  6. 2025Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justicecited by 1
  7. 2025Evaluating the Ability of Large Language Models to Reason about Cardinal Directions, Revisitedcited by 1
  8. 2025Enhancing Reliability in LLM-Integrated Robotic Systems: A Unified Approach to Security and Safetycited by 1
  9. 2026Retcon -- a Prompt-Based Technique for Precise Control of LLMs in Conversationscited by 0