English(EN) Domain-Adaptive Small Language Models for Structured Tax Code Prediction

小型语言模型提高税码预测准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一种具有编码器-解码器架构的领域自适应小型语言模型（SLM），专门用于预测结构化税码。该方法旨在通过捕获协调制度（HSN）和联合国标准产品和服务代码（UNSPSC）等代码中的层级依赖关系，来提高跨国公司税码确定的准确性。实验表明，SLM 在此专业序列生成任务上优于扁平分类器和其他编码器-解码器架构。 AI

影响这项研究可能通过自动化税码预测，为企业的税务合规系统带来更高效、更准确的解决方案。

排序理由该集群包含一篇学术论文，详细介绍了新的模型架构及其在特定领域的应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Souvik Nath, Sumit Wadhwa, Luis Perez · 2026-11-05 05:00

面向结构化税法预测的域自适应小型语言模型

arXiv:2507.10880v3 Announce Type: replace-cross Abstract: Every day, multinational firms process thousands of transactions, each of which must adhere to tax regulations that vary by jurisdiction and are often nuanced. The determination of product and service tax codes, such as HS…

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面向结构化税法预测的域自适应小型语言模型

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