Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity
Despite the growing capabilities of large language models (LLMs), prompt design remains largely heuristic and ad hoc. This project will explore $\textit{prompt minimization}$, the process of reducing prompts to their smallest, most information-dense form while preserving output fidelity. Practically, shorter prompts re...
Marius F. R. Juston, Kevin A. Karim, Jonathan Gao et al.
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