用大模型自动分类商品，提升效率与标准化水平。
Leveraging Large Language Models For Optimized Item Categorization using UNSPSC Taxonomy
- 利用大语言模型分析商品描述，自动匹配UNSPSC编码
- 相比人工分类，准确率高且大幅减少工作量
- 适合需要统一商品管理的企业与数据团队
有效的商品分类对企业发展至关重要，可将非结构化数据转化为有序类别，从而优化库存管理。尽管如此，商品分类仍高度依赖主观判断，行业间缺乏统一标准。联合国产品与服务代码（UNSPSC）提供了一套标准化的分类体系，但实际应用中常需大量人工操作。本文研究大语言模型（LLMs）在基于商品描述自动分类至UNSPSC编码中的应用，评估其在不同数据集上的分类准确率与效率，探索其语言处理能力及作为标准化工具的潜力。结果表明，大语言模型可显著降低人工投入，同时保持高准确性，为希望提升库存管理的企业提供可扩展的解决方案。
原文摘要 · Abstract (English)
Effective item categorization is vital for businesses, enabling the transformation of unstructured datasets into organized categories that streamline inventory management. Despite its importance, item categorization remains highly subjective and lacks a uniform standard across industries and businesses. The United Nations Standard Products and Services Code (UNSPSC) provides a standardized system for cataloguing inventory, yet employing UNSPSC categorizations often demands significant manual effort. This paper investigates the deployment of Large Language Models (LLMs) to automate the classification of inventory data into UNSPSC codes based on Item Descriptions. We evaluate the accuracy and efficiency of LLMs in categorizing diverse datasets, exploring their language processing capabilities and their potential as a tool for standardizing inventory classification. Our findings reveal that LLMs can substantially diminish the manual labor involved in item categorization while maintaining high accuracy, offering a scalable solution for businesses striving to enhance their inventory management practices.
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。