论文标题
从自然语言到模拟：应用GPT-3法典以自动化物流系统的模拟建模
From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics Systems
论文作者
论文摘要
我们的工作是使用自然语言处理以自动化对物流至关重要的系统模拟模型的开发的首次尝试。我们证明，基于变压器的语言模型的微型GPT-3 Codex的顶部构建的框架可以在给定口头描述的情况下，对排队和库存控制系统进行功能有效的模拟。在进行的实验中，GPT-3 Codex在Python上表现出了令人信服的专业知识，并且对特定于域的词汇有了了解。结果，语言模型可以在给定特定领域的上下文，详细描述过程以及具有相应值的变量列表的情况下，制作单产品库存控制系统和单服务器排队系统的模拟。展示的结果以及语言模型的快速改进，为模拟模型开发背后的工作流程的重大简化打开了大门，这将使专家可以专注于对问题和整体思维的高级考虑。
Our work is the first attempt to apply Natural Language Processing to automate the development of simulation models of systems vitally important for logistics. We demonstrated that the framework built on top of the fine-tuned GPT-3 Codex, a Transformer-based language model, could produce functionally valid simulations of queuing and inventory control systems given the verbal description. In conducted experiments, GPT-3 Codex demonstrated convincing expertise in Python as well as an understanding of the domain-specific vocabulary. As a result, the language model could produce simulations of a single-product inventory-control system and single-server queuing system given the domain-specific context, a detailed description of the process, and a list of variables with the corresponding values. The demonstrated results, along with the rapid improvement of language models, open the door for significant simplification of the workflow behind the simulation model development, which will allow experts to focus on the high-level consideration of the problem and holistic thinking.
