从指令到输出：提示在现代自然语言生成中的作用
From Instruction to Output: The Role of Prompting in Modern NLG
AI总结：
本文探讨了提示工程在自然语言生成中的作用，提出了提示范式的分类和决策框架，旨在提升NLG的可控性和可推广性。
摘要：
提示工程已经成为扩展大型语言模型（LLMs）优势和能力的重要技术，以在各种自然语言处理（NLP）任务中获得显著性能提升。这种方法要求用自然语言编写指令，以结构化的方式从LLMs中提取知识，已在各种NLP任务中推动了突破。然而，目前仍缺乏结构化的框架或对多种提示工程方法和技术的系统理解，尤其是在自然语言生成（NLG）领域。本文旨在通过概述提示工程的最新发展及其对不同NLG任务的影响来填补这一空白。它回顾了最近的提示方法及其对NLG任务的影响，将提示设计作为输入层面的控制机制，补充微调和解码方法。本文介绍了提示范式的分类法，为从业者提供了基于不同因素的提示选择决策框架，概述了新兴趋势和挑战，并提出了一种将设计、优化和评估联系起来的框架，以支持更可控和可推广的NLG。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
