Effectively utilizing 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, all supported by clicking, dragging, or text editing, to achieve more controllable and stable responses. Through visual interaction with a graphical user interface, users can incorporate their ideas into the workflow without writing trivial prompts. The proposed Low-code LLM framework consists of a Planning LLM that designs a structured planning workflow for complex tasks, which can be correspondingly edited and confirmed by users through low-code visual programming operations, and an Executing LLM that generates responses following the user-confirmed workflow. We highlight three advantages of the low-code LLM: controllable generation results, user-friendly human-LLM interaction, and broadly applicable scenarios. We demonstrate its benefits using four typical applications. By introducing this approach, we aim to bridge the gap between humans and LLMs, enabling more effective and efficient utilization of LLMs for complex tasks. Our system will be soon publicly available at LowCodeLLM.

翻译：有效利用大语言模型处理复杂任务颇具挑战性，往往涉及耗时且不可控的提示工程过程。本文提出了一种新型人机交互框架——低代码大语言模型。该框架整合了六种基于点击、拖拽或文本编辑的简单低代码视觉编程交互方式，以实现更可控且稳定的响应。通过图形用户界面的可视化交互，用户无需编写琐碎的提示即可将自身想法融入工作流。所提出的低代码大语言模型框架包含两个核心组件：规划模型负责为复杂任务设计结构化规划工作流，用户可通过低代码视觉编程操作对该工作流进行相应编辑和确认；执行模型则依据用户确认的工作流生成响应。我们重点阐述了低代码大语言模型的三大优势：可控的生成结果、友好的用户交互以及广泛的应用场景。通过四个典型应用案例验证其效能，旨在弥合人类与大语言模型之间的鸿沟，使复杂任务中的大语言模型利用更高效。该系统即将在LowCodeLLM平台公开发布。