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From Instruction to Output: The Role of Prompting in Modern NLG

Munazza Zaib, Elaf Alhazmi

TL;DR

This paper surveys prompting for natural language generation (NLG), arguing that prompt design offers an efficient input-level control alternative to fine-tuning and decoding-based methods. It introduces a three-dimensional framework—design, optimization, and evaluation—to formalize how prompts steer content, structure, and style in output, and provides a taxonomy of paradigms spanning foundational, contextual, and advanced reasoning approaches. The authors discuss evaluation strategies, robustness, and emerging trends such as retrieval-augmented prompting and multilingual prompting, while identifyingopen challenges like brittleness, bias amplification, and factuality. The work advocates a systematic, modular approach to prompt engineering, emphasizing portability, reproducibility, and principled design to enable reliable NLG systems without full retraining.

Abstract

Prompt engineering has emerged as an integral technique for extending the strengths and abilities of Large Language Models (LLMs) to gain significant performance gains in various Natural Language Processing (NLP) tasks. This approach, which requires instructions to be composed in natural language to bring out the knowledge from LLMs in a structured way, has driven breakthroughs in various NLP tasks. Yet there is still no structured framework or coherent understanding of the varied prompt engineering methods and techniques, particularly in the field of Natural Language Generation (NLG). This survey aims to help fill that gap by outlining recent developments in prompt engineering, and their effect on different NLG tasks. It reviews recent advances in prompting methods and their impact on NLG tasks, presenting prompt design as an input-level control mechanism that complements fine-tuning and decoding approaches. The paper introduces a taxonomy of prompting paradigms, a decision framework for prompt selection based on varying factors for the practitioners, outlines emerging trends and challenges, and proposes a framework that links design, optimization, and evaluation to support more controllable and generalizable NLG.

From Instruction to Output: The Role of Prompting in Modern NLG

TL;DR

This paper surveys prompting for natural language generation (NLG), arguing that prompt design offers an efficient input-level control alternative to fine-tuning and decoding-based methods. It introduces a three-dimensional framework—design, optimization, and evaluation—to formalize how prompts steer content, structure, and style in output, and provides a taxonomy of paradigms spanning foundational, contextual, and advanced reasoning approaches. The authors discuss evaluation strategies, robustness, and emerging trends such as retrieval-augmented prompting and multilingual prompting, while identifyingopen challenges like brittleness, bias amplification, and factuality. The work advocates a systematic, modular approach to prompt engineering, emphasizing portability, reproducibility, and principled design to enable reliable NLG systems without full retraining.

Abstract

Prompt engineering has emerged as an integral technique for extending the strengths and abilities of Large Language Models (LLMs) to gain significant performance gains in various Natural Language Processing (NLP) tasks. This approach, which requires instructions to be composed in natural language to bring out the knowledge from LLMs in a structured way, has driven breakthroughs in various NLP tasks. Yet there is still no structured framework or coherent understanding of the varied prompt engineering methods and techniques, particularly in the field of Natural Language Generation (NLG). This survey aims to help fill that gap by outlining recent developments in prompt engineering, and their effect on different NLG tasks. It reviews recent advances in prompting methods and their impact on NLG tasks, presenting prompt design as an input-level control mechanism that complements fine-tuning and decoding approaches. The paper introduces a taxonomy of prompting paradigms, a decision framework for prompt selection based on varying factors for the practitioners, outlines emerging trends and challenges, and proposes a framework that links design, optimization, and evaluation to support more controllable and generalizable NLG.
Paper Structure (40 sections, 1 figure, 2 tables)

This paper contains 40 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Positioning and Distinct Contributions
  3. How this survey differs?
  4. What readers gain?
  5. Comparison with Fine-Tuning and Decoding-Level Control
  6. Taxonomy of Prompting Techniques
  7. Foundational Paradigms
  8. Zero-shot Prompting ZeroShot2019RadfordEtAl.
  9. Few-shot Prompting FewShot2020Brown2020EtAl.
  10. Chain-of-thought (CoT) Prompting CoT2022WeiEtAl.
  11. Role Prompting RolePrompting2023KongEtAl.
  12. Contextual Paradigms
  13. Thread-of-thought (ThoT) Prompting ThoT2023ZhouEtAl.
  14. Chain-of-event (CoE) Prompting CoE2024BaoEtAl.
  15. Advanced Reasoning Paradigms
  16. ...and 25 more sections

Figures (1)

  • Figure 1: Decision framework for selecting prompt-engineering strategies for NLG based on task complexity, interaction setting, primary control objective, and resource constraints.