XBRLTagRec: Domain-Specific Fine-Tuning and Zero-Shot Re-Ranking with LLMs for Extreme Financial Numeral Labeling

TL;DR

XBRLTagRec is a novel framework that combines fine-tuned language models and zero-shot re-ranking with ChatGPT to improve financial numeral tagging accuracy in XBRL data, outperforming existing methods.

Contribution

The paper introduces XBRLTagRec, integrating semantic retrieval and zero-shot re-ranking with LLMs for enhanced financial tag matching accuracy.

Findings

XBRLTagRec achieves 2.64%-4.47% higher Hits@1 and Macro metrics than previous methods.

The framework effectively handles large-scale, semantically complex financial tagging tasks.

Experimental results validate the superiority of combining fine-tuning with zero-shot re-ranking.

Abstract

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.