April 2, 2024, 7:52 p.m. | Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, Wang You, Ting Song, Yan Xia, Jonathan Tien, Nan Duan, Furu Wei

cs.CL updates on arXiv.org arxiv.org

arXiv:2304.08103v3 Announce Type: replace
Abstract: Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. Through visual interaction with a graphical user interface, users can incorporate their ideas into the process without writing trivial prompts. The proposed Low-code LLM framework consists of a Planning …

arxiv code code llm cs.cl cs.hc language language models large language large language models llm low low-code type

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