Papers with GenPI
Generative Prompt Internalization (2025.naacl-long)
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| Challenge: | Prompts used in large language model based applications are often fixed and lengthy, leading to significant computational overhead. |
| Approach: | They propose a method that internalizes complex prompts using a joint training approach and a data synthesis technique that auto-collects conversational datasets by swapping roles of agent and environment. |
| Outcome: | The proposed method internalizes complex prompts across agent-based applications and generates the content along with reasons for why it should change accordingly. |