Reverse Prompt Engineering

• URL: http://arxiv.org/abs/2411.06729v1
• Date: Mon, 11 Nov 2024 05:58:48 GMT
• Title: Reverse Prompt Engineering
• Authors: Hanqing Li, Diego Klabjan,
• Abstract summary: This paper explores a new black-box, zero-shot language model inversion problem. It proposes an innovative framework for prompt reconstruction using only text outputs from a language model.
• Score: 12.46661880219403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores a new black-box, zero-shot language model inversion problem and proposes an innovative framework for prompt reconstruction using only text outputs from a language model. Leveraging a large language model alongside an optimization algorithm, the proposed method effectively recovers prompts with minimal resources. Experimental results on several datasets derived from public sources indicate that the proposed approach achieves high-quality prompt recovery and generates prompts more similar to the originals than current state-of-the-art methods. Additionally, the use-case study demonstrates the method's strong potential for generating high-quality text data.

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