2023 PromptEngineeringaPromptEnginee
- (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
Subject Headings: Auto Prompt Engineering, Prompt Engineering System.
Notes
Based on the detailed analysis of the research paper titled "Prompt Engineering a Prompt Engineer," the following seven bullet points summarize the key findings and contributions of the study:
- 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]] experiments with verbalized counterparts of common optimization concepts (batch size, step size, momentum) within the meta-prompt and examines their impact on LLMs' performance in prompt engineering tasks.
- 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]] provides empirical evidence that certain components of the meta-prompt, such as detailed instructions, context specification, and optimizer-inspired concepts, contribute significantly to the quality of prompt engineering.
- 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.
- It]] suggests that future work could explore optimizing the meta-prompt itself in a self-referential manner to further enhance the effectiveness of automatic prompt engineering.
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Quotes
Abstract
Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models (LLMs). It requires complex reasoning to examine the model's errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that LLMs can be meta-prompted to perform automatic prompt engineering, their potentials may not be fully untapped due to the lack of sufficient guidance to elicit complex reasoning capabilities in LLMs in the meta-prompt. In this work, we investigate the problem of "prompt engineering a prompt engineer" -- constructing a meta-prompt that more effectively guides LLMs to perform automatic prompt engineering. We introduce and analyze key components, such as a step-by-step reasoning template and context specification, which lead to improved performance. In addition, inspired by common optimization concepts such as batch size, step size and momentum, we introduce their verbalized counterparts to the meta-prompt and investigate their effects. Our final method, named PE2, finds a prompt that outperforms "let's think step by step" by 6.3% on the MultiArith dataset and 3.1% on the GSM8K dataset. To demonstrate its versatility, we apply PE2 to the Instruction Induction benchmark, a suite of counterfactual tasks, and a lengthy, real-world industrial prompt. In these settings, PE2 achieves strong performance and outperforms prior automatic prompt engineering baselines. Further, we show that PE2 makes meaningful and targeted prompt edits, amends erroneous or incomplete prompts, and presents non-trivial counterfactual reasoning abilities.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 PromptEngineeringaPromptEnginee | Fereshte Khani Qinyuan Ye Maxamed Axmed Reid Pryzant | Prompt Engineering a Prompt Engineer | 10.48550/arXiv.2311.05661 | 2023 |