大型语言模型能否作为工业规模下科学文档分层多标签分类的有效分类器？
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?
AI总结：
针对工业规模下科学文档分层多标签分类（HMC）的动态标签与分类体系挑战，提出结合大型语言模型（LLMs）与密集检索的零样本HMC方法，在SSRN数据集上提升准确率与成本效率，为动态分类体系下的文档分类提供关键见解。
摘要：
我们针对工业规模下科学文档的分层多标签分类（HMC）任务展开研究，该任务需将数十万篇文档分类到数千个动态标签中。科学出版物的快速增长需要可扩展且高效的分类方法，而分类体系的动态性——如新类别引入、现有类别合并、过时类别淘汰——进一步加剧了复杂性。传统机器学习方法每次分类体系更新都需昂贵的重新训练，由于标签数据收集和模型适配的高昂开销，变得不切实际。大型语言模型（LLMs）在多标签分类等复杂任务中展现出巨大潜力，但应用于大型动态分类体系时面临独特挑战，因为大量标签可能超出LLMs的输入限制。本文提出结合LLMs优势与密集检索技术的新方法以克服这些挑战。我们的方法通过利用零样本HMC进行实时标签分配，避免了重新训练。我们在SSRN（一个涵盖多学科的大型预印本库）上评估方法的有效性，证明在分类准确率和成本效率上均有显著提升。通过开发针对动态分类体系的定制评估框架并公开代码，本研究为工业规模下将LLMs应用于文档分类提供了关键见解，其中类别数量对应大型分类体系中的节点数。
英文摘要：
We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
