用AI自动生成政府战略规划中的核心主题，准确率达100%。
Generative AI for Strategic Plan Development
- 用BERTopic和NMF对政府报告做主题建模，提取战略主题。
- 生成主题与官方战略元素匹配度达100%，BERTopic表现更优。
- 适合政府、公共政策与AI融合研究者参考。
随着生成式人工智能（GAI）和大语言模型（LLM）的突破，越来越多专业服务正被AI增强，曾被认为难以自动化的任务也逐渐可行。本文提出一种模块化模型，用于在大型政府组织中利用GAI开发战略规划，并评估了主流机器学习技术在其中某一模块的应用效果。具体而言，对比了BERTopic与非负矩阵分解（NMF）在主题建模中识别战略规划中‘愿景要素’主题的能力。模型使用美国问责总署（GAO）大量报告进行训练，生成的主题与已发布战略规划中的愿景要素进行相似性评分并比较。结果表明，这些技术能够生成与100%被评估要素相似的主题；其中BERTopic表现最佳，超过一半相关主题达到‘中等’或‘强’相关性。该能力可影响数十亿美元产业，助力联邦政府应对关键监管要求，促进公共利益。后续工作将聚焦于本研究证明概念的落地应用，以及模型其余模块在GAI生成战略规划中的可行性。
原文摘要 · Abstract (English)
Given recent breakthroughs in Generative Artificial Intelligence (GAI) and Large Language Models (LLMs), more and more professional services are being augmented through Artificial Intelligence (AI), which once seemed impossible to automate. This paper presents a modular model for leveraging GAI in developing strategic plans for large scale government organizations and evaluates leading machine learning techniques in their application towards one of the identified modules. Specifically, the performance of BERTopic and Non-negative Matrix Factorization (NMF) are evaluated in their ability to use topic modeling to generate themes representative of Vision Elements within a strategic plan. To accomplish this, BERTopic and NMF models are trained using a large volume of reports from the Government Accountability Office (GAO). The generated topics from each model are then scored for similarity against the Vision Elements of a published strategic plan and the results are compared. Our results show that these techniques are capable of generating themes similar to 100% of the elements being evaluated against. Further, we conclude that BERTopic performs best in this application with more than half of its correlated topics achieving a "medium" or "strong" correlation. A capability of GAI-enabled strategic plan development impacts a multi-billion dollar industry and assists the federal government in overcoming regulatory requirements which are crucial to the public good. Further work will focus on the operationalization of the concept proven in this study as well as viability of the remaining modules in the proposed model for GAI-generated strategic plans.
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