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

TL;DR

This paper surveys recent developments in prompt engineering for Large Language Models, highlighting its role in enhancing Natural Language Generation tasks and proposing a structured framework for understanding and applying prompting techniques.

Contribution

It introduces a taxonomy of prompting paradigms, a decision framework for prompt selection, and links design, optimization, and evaluation to improve NLG control and generalization.

Findings

Prompt engineering significantly improves NLG performance.

A taxonomy and decision framework aid in prompt selection.

Emerging trends and challenges are identified.

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.