Prompt recovery in large language models (LLMs) is crucial for understanding
how LLMs work and addressing concerns regarding privacy, copyright, etc. The
trend towards inference-only APIs complicates this task by restricting access
to essential outputs for recovery. To tackle this challenge, we extract
prompt-related information from limited outputs and identify a strong(negative)
correlation between output probability-based uncertainty and the success of
prompt recovery. This finding led to the development of Deliberative PrOmpt
RecoverY (DORY), our novel approach that leverages uncertainty to recover
prompts accurately. DORY involves reconstructing drafts from outputs, refining
these with hints, and filtering out noise based on uncertainty. Our evaluation
across diverse LLMs and prompt benchmarks shows that DORY outperforms existing
baselines, improving performance by approximately 10.82% and establishing a new
state-of-the-art record in prompt recovery tasks. Significantly, DORY operates
using a single LLM without any external resources or model, offering a
cost-effective, user-friendly prompt recovery solution.

利用输出概率的不确定性构建新的方法 Deliberative PrOmpt RecoverY 来恢复模型的提示，通过改进和筛选提高了大型语言模型的性能，成为提示恢复任务中的标杆。