Summary of On the Transformations Across Reward Model, Parameter Update, and In-context Prompt, by Deng Cai and Huayang Li and Tingchen Fu and Siheng Li and Weiwen Xu and Shuaiyi Li and Bowen Cao and Zhisong Zhang and Xinting Huang and Leyang Cui and Yan Wang and Lemao Liu and Taro Watanabe and Shuming Shi
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
by Deng Cai, Huayang Li, Tingchen Fu, Siheng Li, Weiwen Xu, Shuaiyi Li, Bowen Cao, Zhisong Zhang, Xinting Huang, Leyang Cui, Yan Wang, Lemao Liu, Taro Watanabe, Shuming Shi
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors explore the adaptability of pre-trained large language models (LLMs) to real-world applications by demonstrating the interchangeability of three popular adaptation tools: parameter updating, reward modeling, and in-context prompting. The researchers establish a triangular framework with six transformation directions that facilitate various practical applications. This work offers a unified view of existing studies and suggests potential research directions for future development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools, but they need to be adapted to real-world tasks. In this paper, scientists show that three different ways to adapt LLMs – updating the model, giving it rewards, or asking specific questions – can all work together. This helps us understand how these models can be used in many different applications. The research also shows how existing studies fit together and suggests new areas to explore. |
Keywords
» Artificial intelligence » Prompting
