用AI自动生成代码来实现用户意图，提升人机协作效率。
Towards Machine-Generated Code for the Resolution of User Intentions
- 用户输入意图，AI生成可执行代码完成任务。
- GPT-4o-mini在生成代码流程上表现优异。
- 适合希望自动化操作的开发者与普通用户。
人工智能，尤其是大语言模型（LLMs）能力的提升，促使我们重新思考用户与设备之间的交互方式。当前用户需通过一系列高层应用达成目标，而AI的发展为基于模型生成代码实现用户意图提供了新可能。本文探索通过提示大语言模型，结合无GUI操作系统简化接口，自动生成并执行工作流的可行性。研究对多种用户意图、生成代码及其执行效果进行了深入分析与对比。结果表明该方法具有普遍可行性，所用模型GPT-4o-mini在根据用户意图生成代码化工作流方面表现出显著能力。
原文摘要 · Abstract (English)
The growing capabilities of Artificial Intelligence (AI), particularly Large Language Models (LLMs), prompt a reassessment of the interaction mechanisms between users and their devices. Currently, users are required to use a set of high-level applications to achieve their desired results. However, the advent of AI may signal a shift in this regard, as its capabilities have generated novel prospects for user-provided intent resolution through the deployment of model-generated code. This development represents a significant progression in the realm of hybrid workflows, where human and artificial intelligence collaborate to address user intentions, with the former responsible for defining these intentions and the latter for implementing the solutions to address them. In this paper, we investigate the feasibility of generating and executing workflows through code generation that results from prompting an LLM with a concrete user intention, and a simplified application programming interface for a GUI-less operating system. We provide an in-depth analysis and comparison of various user intentions, the resulting code, and its execution. The findings demonstrate the general feasibility of our approach and that the employed LLM, GPT-4o-mini, exhibits remarkable proficiency in the generation of code-oriented workflows in accordance with provided user intentions.
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