On the Transformations across Reward Model, Parameter Update, and
  In-Context Prompt
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.
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Huayang Li
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Tingchen Fu
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Tingchen Fu
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Siheng Li
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Weiwen Xu
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Shuaiyi Li
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Li Shuaiyi
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Bowen Cao
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tim huang
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Leyang Cui
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Yan Wang
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Lemao Liu
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Shuming Shi
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Shuming Shi
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Shuming
On the Transformations across Reward Model, Parameter Update, and
  In-Context Prompt
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.
Three adaptation tools—parameter updating, reward modeling, and in-context prompting—are interchangeable in LLMs, forming a framework that supports multiple applications and guides future research.
pre-trained large language models parameter updating reward modeling in-context prompting