April 2, 2024, 7:52 p.m. | Kaiyan Chang, Songcheng Xu, Chenglong Wang, Yingfeng Luo, Tong Xiao, Jingbo Zhu

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.01077v1 Announce Type: new
Abstract: Prompting has become a mainstream paradigm for adapting large language models (LLMs) to specific natural language processing tasks. While this approach opens the door to in-context learning of LLMs, it brings the additional computational burden of model inference and human effort of manual-designed prompts, particularly when using lengthy and complex prompts to guide and control the behavior of LLMs. As a result, the LLM field has seen a remarkable surge in efficient prompting methods. In …

abstract arxiv become computational context cs.cl human in-context learning inference language language models language processing large language large language models llms natural natural language natural language processing paradigm processing prompting prompts survey tasks type

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