Feb. 28, 2024, 5:49 a.m. | Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang

cs.CL updates on arXiv.org arxiv.org

arXiv:2309.08532v2 Announce Type: replace
Abstract: Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EvoPrompt, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, …

abstract algorithms arxiv automate cs.ai cs.cl demand evolutionary algorithms excel framework human language language models large language large language models llms novel optimization paper process prompt prompts tasks type

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