Summary of Generative Prompt Internalization, by Haebin Shin et al.
Generative Prompt Internalization
by Haebin Shin, Lei Ji, Yeyun Gong, Sungdong Kim, Eunbi Choi, Minjoon Seo
First submitted to arxiv on: 24 Nov 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 The paper proposes a lightweight method called Generative Prompt Internalization (GenPI) to address the challenge of fixed and lengthy prompts in large language model-based applications. GenPI uses a joint training approach to replicate the behavior of models with prompt inputs while also generating the content of the prompts along with reasons for why the model’s behavior should change accordingly. The method is demonstrated to be effective across various agent-based application scenarios, and a data synthesis technique is introduced to autonomously collect conversational datasets by swapping the roles of the agent and environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help large language models understand what they’re supposed to do without needing long prompts. This is called Generative Prompt Internalization (GenPI). It’s like teaching the model how to think about what it needs to do, rather than just telling it exactly what to do. The authors show that this approach works well in different scenarios and can even collect its own training data by switching roles with the environment. |
Keywords
» Artificial intelligence » Large language model » Prompt
