系统梳理提示工程在自然语言生成中的作用与方法。
From Instruction to Output: The Role of Prompting in Modern NLG
- 提出提示工程的分类体系与选择框架
- 揭示提示设计对生成质量的关键影响
- 适合想高效使用大模型的开发者与研究者
提示工程已成为拓展大型语言模型（LLMs）能力的核心技术，在多个自然语言处理（NLP）任务中带来显著性能提升。该方法通过自然语言指令引导模型结构化输出知识，推动了多项NLP任务的突破。然而，提示工程方法缺乏系统性框架与统一理解，尤其在自然语言生成（NLG）领域尤为明显。本文旨在填补这一空白，综述近期提示工程进展及其对不同NLG任务的影响。文章将提示设计视为输入层控制机制，补充微调与解码策略，提出提示范式的分类体系，建立基于多因素的提示选择决策框架，分析新兴趋势与挑战，并构建涵盖设计、优化与评估的一体化框架，以实现更可控、可泛化的自然语言生成。
原文摘要 · Abstract (English)
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.
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