  Prompt recovery, a crucial task in natural language processing, entails the
reconstruction of prompts or instructions that language models use to convert
input text into a specific output. Although pivotal, the design and
effectiveness of prompts represent a challenging and relatively untapped field
within NLP research. This paper delves into an exhaustive investigation of
prompt recovery methodologies, employing a spectrum of pre-trained language
models and strategies. Our study is a comparative analysis aimed at gauging the
efficacy of various models on a benchmark dataset, with the goal of pinpointing
the most proficient approach for prompt recovery. Through meticulous
experimentation and detailed analysis, we elucidate the outstanding performance
of the Gemma-2b-it + Phi2 model + Pretrain. This model surpasses its
counterparts, showcasing its exceptional capability in accurately
reconstructing prompts for text transformation tasks. Our findings offer a
significant contribution to the existing knowledge on prompt recovery, shedding
light on the intricacies of prompt design and offering insightful perspectives
for future innovations in text rewriting and the broader field of natural
language processing.
