通过大型语言模型将商业活动分类为国际标准产业分类以促进循环经济的统一框架
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
AI总结：
本文提出利用大型语言模型将企业经济活动文本统一分类到国际标准产业分类（ISIC），以构建集中式知识库支持循环经济推荐系统，并在182标签测试集上使用微调GPT-2达到95%准确率。
摘要：
有效的信息收集和知识编码对于开发促进循环经济实践的推荐系统至关重要。一种有前景的方法涉及创建一个集中式知识库，对历史上的废物到资源交易进行编目，从而能够基于过去的成功案例生成推荐。然而，构建此类知识库的一个重大障碍在于缺乏一个普遍标准化的框架来表示不同地理区域的商业活动。为了应对这一挑战，本文利用大型语言模型（LLMs）将描述经济活动的文本数据分类到国际标准产业分类（ISIC）中，这是一个全球公认的经济活动分类框架。这种方法使全球企业提供的任何经济活动描述都能被归类到统一的ISIC标准中，从而促进集中式知识库的创建。我们的方法在包含182个标签的测试数据集上使用微调的GPT-2模型达到了95%的准确率。这项研究通过为跨区域可部署的知识编码和推荐系统提供标准化基础，为促进可持续循环经济实践的全球努力做出了贡献。
英文摘要：
Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the International Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions.
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