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interior scene of a modern, well-lit library with a diverse group of professionals conducting a 'Prompt Engineering' workshop. The group includes a Caucasian male with a beard wearing glasses, an Asian female with short hair, a Black female with curly hair, and a White male with a buzz cut. They are gathered around a round table covered with laptops, papers, and books titled 'Prompt Engineering', 'Optimizing AI', and 'Language Models'. The walls are adorned with infographics explaining principles and methodologies of prompt engineering, and a projector displays a presentation titled 'Applications of Prompt Engineering'.

Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu

Category: “AI for IT”

Article Section: Advanced AI Topics; Natural Language Processing

Publication Date: 2023-10-27

SEO Description: “Review delves into the importance of prompt engineering in optimizing Large Language Models, exploring principles, methodologies, applications, assessment techniques and future research directions.”

Chen, Banghao, et al. Unleashing the Potential of Prompt Engineering in Large Language Models: A Comprehensive Review. arXiv:2310.14735, arXiv, 27 Oct. 2023, http://arxiv.org/abs/2310.14735.

AI-Generated Paper Summary

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The article titled “Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review” outlines how prompt engineering facilitates in exploiting the capabilities of large language models (LLMs). The authors, Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu, delve into the different methodologies of prompt engineering, including role-prompting, one-shot and few-shot prompting, as well as the advanced chain-of-thought and tree-of-thoughts prompting. The paper also discusses the potential future directions in prompt engineering research and highlights the necessity for a more profound understanding of the role of AI-generated content and structures. The authors look into the ways to evaluate the effectiveness of different prompt methods and present an overview of the application of prompt engineering in fields like education and programming.

Claude.ai Full PDF Analysis

Here is an analysis of the academic paper on prompt engineering for large language models:

Novelty:

  • Provides a comprehensive overview of prompt engineering techniques, from basic methods like role prompting to more advanced approaches like chain-of-thought and tree-of-thought prompting.
  • Introduces innovative prompting methods like zero-shot chain-of-thought and golden chain-of-thought.
  • Discusses emerging techniques like generated knowledge prompting and retrieval augmentation to reduce hallucinations.
  • Analyzes future trajectories like understanding model structures and AI agents.
  • Comparatively evaluates subjective and objective assessment of prompts.
  • Overall, offers an extensive synthesis of the state-of-the-art in prompt engineering research.

Commercial Applications:

  • Prompt engineering can be used to optimize LLMs for commercial usage, improving their accuracy and specificity for tasks like customer service chatbots, marketing content generation, etc.
  • Techniques like role prompting allow guiding LLMs to remain coherent in long conversations and ensure responses match a desired persona.
  • Methods to reduce hallucinations could improve reliability of LLMs for high-stakes applications like medical diagnosis.
  • Understanding model structures could allow developing customized prompts tailored to a specific commercial LLM’s architecture.
  • Prompt engineering facilitates easier instruction of LLMs by non-experts, increasing accessibility.
  • Applications like automated data analysis, content creation/editing, and coding can directly benefit from advances in prompt engineering.
  • Overall, prompt engineering unlocks the commercial potential of LLMs by making them more controllable, reliable and accessible.

In summary, this paper offers a comprehensive synthesis of prompt engineering, highlighting novel techniques and trajectories, while also discussing applications across sectors like education, programming etc. The survey can serve as a useful reference for future research and commercial usage of large language models.

Keywords

Prompt Engineering, Large Language Models, Artificial Intelligence-Generated Content, Machine Hallucination, Chain-of-Thought prompting

Author’s Abstract

This paper delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). Prompt engineering is the process of structuring input text for LLMs and is a technique integral to optimizing the efficacy of LLMs. This survey elucidates foundational principles of prompt engineering, such as role-prompting, one-shot, and few-shot prompting, as well as more advanced methodologies such as the chain-of-thought and tree-of-thoughts prompting. The paper sheds light on how external assistance in the form of plugins can assist in this task, and reduce machine hallucination by retrieving external knowledge. We subsequently delineate prospective directions in prompt engineering research, emphasizing the need for a deeper understanding of structures and the role of agents in Artificial Intelligence-Generated Content (AIGC) tools. We discuss how to assess the efficacy of prompt methods from different perspectives and using different methods. Finally, we gather information about the application of prompt engineering in such fields as education and programming, showing its transformative potential. This comprehensive survey aims to serve as a friendly guide for anyone venturing through the big world of LLMs and prompt engineering.

Read the full paper here

Last updated on November 5th, 2023.