On the Transformations across Reward Model, Parameter Update, and
  In-Context Prompt
Authors: name: Deng Cai, name: Huayang Li, name: Tingchen Fu, name: Siheng Li, name: Weiwen Xu, name: Shuaiyi Li, name: Bowen Cao, name: Zhisong Zhang, name: Xinting Huang, name: Leyang Cui, name: Yan Wang, name: Lemao Liu, name: Taro Watanabe, name: Shuming Shi

Despite the general capabilities of pre-trained large language models (LLMs),
they still need further adaptation to better serve practical applications. In
this paper, we demonstrate the interchangeability of three popular and distinct
adaptation tools: parameter updating, reward modeling, and in-context
prompting. This interchangeability establishes a triangular framework with six
transformation directions, each of which facilitates a variety of applications.
Our work offers a holistic view that unifies numerous existing studies and
suggests potential research directions. We envision our work as a useful
roadmap for future research on LLMs.