Prompt Engineering System

AKA: Text-to-* Model Prompt Programming System.
Context:
- It can enable the creation, testing, and refinement of LLM Prompts.
- It can range from being a Manual Prompt Engineering System to being an Automated Prompt Engineering Systme.
- It can provide tools for analyzing the performance of different prompts and suggest modifications to improve outcomes.
- ...
Example(s):
- PRewrite, which uses reinforcement learning for optimizing prompts, showing significant improvements over manual and algorithm-generated prompts.
- PE2, which introduces a novel method incorporating step-by-step reasoning templates for automatic prompt engineering.
- ...
Counter-Example(s):
- A Natural Language Processing System that does not support the creation or refinement of prompts.
- A Machine Learning Framework solely focused on model training and evaluation without facilities for prompt engineering.
See: Auto Prompt Engineering, Reinforcement Learning, Large Language Model, Meta-Learning.

References

(Kong et al., 2024) ⇒ Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. (2024). “PRewrite: Prompt Rewriting with Reinforcement Learning.” doi:10.48550/arXiv.2401.08189
- It introduces PRewrite, a tool leveraging reinforcement learning for optimizing prompts in large language model applications.
- It demonstrates superior results across various datasets, outperforming professionally crafted and other algorithm-generated prompts.
- It contributes a novel RL-based framework for prompt optimization that produces effective and human-readable prompts.

(Ye, Axmed et al., 2023) ⇒ Qinyuan Ye, Maxamed Axmed, Reid Pryzant, and Fereshte Khani. (2023). “Prompt Engineering a Prompt Engineer.” doi:10.48550/arXiv.2311.05661
- It addresses the challenge of optimizing LLMs performance through automatic prompt engineering, focusing on the concept of "prompt engineering a prompt engineer" to enhance LLMs' ability to perform automatic prompt engineering more effectively.
- It introduces a novel method named PE2, which incorporates step-by-step reasoning templates and context specification in the meta-prompt to guide LLMs through the prompt engineering process, leading to improved performance.
- It demonstrates PE2's superior performance over existing automatic prompt engineering methods, showing significant improvements on various datasets, including MultiArith, GSM8K, and a real-world industrial prompt.
- It identifies limitations and failure cases of PE2, revealing challenges such as the LLMs' tendency to neglect given instructions or to hallucinate incorrect rationales, highlighting areas for future improvement.