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Law&Order框架在税务法律自动形式化方面达到100%的准确率

研究人员开发了一个名为Law&Order的神经符号框架,用于将税务法律自动翻译成可执行的符号程序。该系统结合了大型语言模型与单元级验证以及使用人工编写的纳税申报表进行迭代纠错。该框架在保留的纳税申报表上达到了100%的准确率,显著优于仅达到66%准确率的独立LLM。 AI

影响 展示了一种形式化复杂法律文本的新方法,有望加速法律和金融应用的AI系统的开发。

排序理由 详细介绍AI驱动的法律文本形式化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Law&Order框架在税务法律自动形式化方面达到100%的准确率

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详细介绍AI驱动的法律文本形式化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sophia Simeng Han, Yoshiki Takashima, Anjiang Wei, Zhaoyu Li, Michael Genesereth ·

    法律与秩序:税法自动形式化

    arXiv:2610.02792v1 Announce Type: new Abstract: Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations remain underdeveloped. We study this problem through tax law, where forms and filin…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    法律与秩序:税法自动形式化

    Legal systems are increasingly implemented through software, yet scalable methods for translating legal texts into accurate symbolic representations remain underdeveloped. We study this problem through tax law, where forms and filing instructions define large computational struct…