Publicly traded companies must disclose financial information under regulations of the Securities and Exchange Commission (SEC) and the Generally Accepted Accounting Principles (GAAP). The eXtensible Business Reporting Language (XBRL), as an XML-based financial language, enables standardized and machine-readable reporting, but accurate tag selection from large taxonomies remains challenging. Existing fine-tuning-based methods struggle to distinguish highly similar XBRL tags, limiting performance in financial data matching. To address these issues, we introduce XBRLTagRec, an end-to-end framework for automated financial numeral tagging. The framework generates semantic tag documents with a fine-tuned FLAN-T5-Large model, retrieves relevant candidates via semantic similarity, and applies zero-shot re-ranking with ChatGPT-3.5 to select the optimal tag. Experiments on the FNXL dataset show that XBRLTagRec outperforms the state-of-the-art FLAN-FinXC framework, achieving 2.64%-4.47% improvements in Hits@1 and Macro metrics. These results demonstrate its effectiveness in large-scale and semantically complex tag matching scenarios.

翻译：根据美国证券交易委员会（SEC）法规和公认会计原则（GAAP）的要求，上市公司必须披露财务信息。可扩展商业报告语言（XBRL）作为一种基于XML的财务语言，实现了标准化和机器可读的报告形式，但如何从庞大的分类体系中准确选择标签仍具挑战性。现有的基于微调的方法难以区分高度相似的XBRL标签，限制了金融数据匹配的性能。为解决这些问题，我们提出XBRLTagRec——一个端到端的自动金融数字标注框架。该框架通过微调后的FLAN-T5-Large模型生成语义标签文档，利用语义相似度检索相关候选标签，并采用基于ChatGPT-3.5的零样本重排序方法选择最优标签。在FNXL数据集上的实验表明，XBRLTagRec在Hits@1和Macro指标上分别提升2.64%-4.47%，优于当前最优的FLAN-FinXC框架。这些结果验证了其在处理大规模、语义复杂标签匹配场景中的有效性。