揭示隐藏意图：探索提示恢复以深入洞察生成文本
Uncovering Hidden Intentions: Exploring Prompt Recovery for Deeper Insights into Generated Texts
AI总结：
针对AI生成内容研究，本文首次在无封闭任务集场景下探索提示恢复，通过零样本、少样本学习及LoRA微调实验，结合半合成数据集验证，证明可合理准确恢复单模型生成文本的原始提示。
摘要：
如今，AI生成内容的检测正受到越来越多的关注。我们的思路是超越检测范畴，尝试恢复用于生成某段文本的提示词。据我们所知，本文首次在无封闭任务集的特定领域开展相关研究，目标是探究该方法是否具备可行性。我们实验了零样本、少样本上下文学习，还采用了LoRA（低秩适配）微调方法，随后评估了使用半合成数据集的增益。作为首项研究，我们仅针对单一模型生成的文本展开实验。结果表明，以合理的准确度恢复原始提示是可行的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
