Uncovering Hidden Intentions: Exploring Prompt Recovery For Deeper Insights Into Generated Texts

Give Louis, Zaoral Timo, Bruno Maria Antonietta. Arxiv 2024

[Paper]    
Attention Mechanism Few Shot Fine Tuning In Context Learning Model Architecture Pretraining Methods Prompting Training Techniques

Today, the detection of AI-generated content is receiving more and more attention. Our idea is to go beyond detection and try to recover the prompt used to generate a text. This paper, to the best of our knowledge, introduces the first investigation in this particular domain without a closed set of tasks. Our goal is to study if this approach is promising. We experiment with zero-shot and few-shot in-context learning but also with LoRA fine-tuning. After that, we evaluate the benefits of using a semi-synthetic dataset. For this first study, we limit ourselves to text generated by a single model. The results show that it is possible to recover the original prompt with a reasonable degree of accuracy.

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