Advancing Prompt Recovery in NLP: A Deep Dive into the Integration of Gemma-2b-it and Phi2 Models
Jianlong Chen, Wei Xu, Zhicheng Ding, Jinxin Xu, Hao Yan, Xinyu Zhang
TL;DR
The paper tackles prompt recovery in NLP by proposing a novel integration of the Gemma-2b-it backbone with the Phi2 transformer, augmented by a dual-stage pre-training regime. It demonstrates that this combination, especially with domain-specific fine-tuning and perplexity-informed guidance, achieves state-of-the-art prompt reconstruction on a benchmark dataset, with a peak score of 0.61. The work provides concrete architectural and training design choices that deepen understanding of prompt design and model synergy for text rewriting. Practically, it offers a pathway to more accurate and robust prompt generation and recovery, with potential implications for improving controllability and efficiency in large-language-model applications.
Abstract
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.