用大模型自动解析税法修订，实现报税软件的智能更新。
Technical Challenges in Maintaining Tax Prep Software with Large Language Models
- 利用大模型将美国国税局公告转化为可执行代码差异
- 显著降低手动分析税法变更的耗时与出错率
- 适合税务软件开发、法律科技领域从业者参考
随着美国税法随不断变化的政治经济现实持续演进，报税软件在帮助纳税人应对复杂法规方面发挥着关键作用。税法动态变化给准确及时维护报税软件代码带来了巨大挑战。当前主流维护方式依赖人工代码分析与税法专家解读，效率低且易出错。我们提出，税法修订文本具有高度严谨性和形式化特征，适合通过大语言模型（如ChatGPT、Llama）自动翻译为可执行规范（代码）。研究聚焦于识别并解决利用大模型从美国国税局（IRS）出版物中忠实提取代码差异，并自动集成到旧版代码中的技术难题，推动报税软件维护的自动化进程。
原文摘要 · Abstract (English)
As the US tax law evolves to adapt to ever-changing politico-economic realities, tax preparation software plays a significant role in helping taxpayers navigate these complexities. The dynamic nature of tax regulations poses a significant challenge to accurately and timely maintaining tax software artifacts. The state-of-the-art in maintaining tax prep software is time-consuming and error-prone as it involves manual code analysis combined with an expert interpretation of tax law amendments. We posit that the rigor and formality of tax amendment language, as expressed in IRS publications, makes it amenable to automatic translation to executable specifications (code). Our research efforts focus on identifying, understanding, and tackling technical challenges in leveraging Large Language Models (LLMs), such as ChatGPT and Llama, to faithfully extract code differentials from IRS publications and automatically integrate them with the prior version of the code to automate tax prep software maintenance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。