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Large Language Models Are Human-Level Prompt Engineers

Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H. and Ba, J. (2022). Large Language Models Are Human-Level Prompt Engineers. arXiv:2211.01910 [cs]. [online] Available at: https://arxiv.org/abs/2211.01910

General Annotation #

The paper titled “Large Language Models are Human-Level Prompt Engineers” by Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba, and affiliated with the University of Toronto, Vector Institute, and University of Waterloo, introduces a method named Automatic Prompt Engineer (APE). This method automates the generation and selection of prompts to steer Large Language Models (LLMs) towards desired tasks. APE views the prompt as a “program” to be optimized over a pool of candidates proposed by an LLM, aiming to maximize a selected score function. The methodology’s effectiveness is demonstrated through its superior performance over previous LLM baselines and human-crafted prompts across various Instruction Induction tasks and curated BIG-Bench tasks. APE proves effective in not only generating high-quality prompts but also in enhancing few-shot learning, finding better zero-shot chain-of-thought prompts, and steering models towards truthfulness and informativeness.

Methodologies Used #

  • Automatic Prompt Engineer (APE): Automates the instruction generation and selection process, treating the instruction as a program optimized over a pool of instruction candidates proposed by an LLM.
  • Score Functions: Utilizes score functions to evaluate the quality of the selected instruction by evaluating the zero-shot performance of an LLM following the selected instruction.
  • Iterative Monte Carlo Search: Employs an iterative process for resampling instruction candidates to refine the selection process further.

Key Contributions #

  • APE achieves human-level or superior performance in generating instructions for LLMs on 24/24 Instruction Induction tasks and 17/21 curated BIG-Bench tasks.
  • Demonstrates the versatility of APE in enhancing few-shot learning, identifying better zero-shot chain-of-thought prompts, and steering LLMs toward truthfulness and/or informativeness.
  • Provides extensive qualitative and quantitative analyses exploring the effectiveness and applications of APE.

Main Arguments #

  • Emphasizes the significance of prompt engineering in controlling and directing the behavior of LLMs toward desired tasks.
  • Argues for the necessity of automating the prompt engineering process to alleviate the manual effort involved in generating and validating effective instructions.
  • Highlights the capability of APE in surpassing human performance in prompt generation, showcasing the potential of LLMs as autonomous prompt engineers.

Gaps #

  • The exploration of APE’s applicability across wider domains and tasks remains an area for future research.
  • Additional studies are needed to assess the scalability of APE when applied to larger and more complex LLM architectures.

Relevance to Prompt Engineering & Architecture #

The introduction of APE marks a significant advancement in prompt engineering and the architecture of language models. This method streamlines the prompt generation process, showcasing the potential for LLMs to autonomously generate high-quality prompts that direct model behavior towards desired outcomes. APE’s success in various tasks encourages further exploration into automating and optimizing the interaction between humans and AI, with potential implications for enhancing the efficiency and effectiveness of model training and deployment in real-world applications.

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Updated on March 31, 2024