Accurate interpretation of numerical data in financial reports is critical for markets and regulators. Although XBRL (eXtensible Business Reporting Language) provides a standard for tagging financial figures, mapping thousands of facts to over 10k US GAAP concepts remains costly and error prone. Existing benchmarks oversimplify this task as flat, single step classification over small subsets of concepts, ignoring the hierarchical semantics of the taxonomy and the structured nature of financial documents. Consequently, these benchmarks fail to evaluate Large Language Models (LLMs) under realistic reporting conditions. To bridge this gap, we introduce FinTagging, the first comprehensive benchmark for structure aware and full scope XBRL tagging. We decompose the complex tagging process into two subtasks: (1) FinNI (Financial Numeric Identification), which extracts entities and types from heterogeneous contexts including text and tables; and (2) FinCL (Financial Concept Linking), which maps extracted entities to the full US GAAP taxonomy. This two stage formulation enables a fair assessment of LLMs' capabilities in numerical reasoning and taxonomy alignment. Evaluating diverse LLMs in zero shot settings reveals that while models generalize well in extraction, they struggle significantly with fine grained concept linking, highlighting critical limitations in domain specific structure aware reasoning.
@article{arxiv.2505.20650,
title = {FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information},
author = {Yan Wang and Lingfei Qian and Xueqing Peng and Yang Ren and Keyi Wang and Yi Han and Dongji Feng and Fengran Mo and Shengyuan Lin and Qinchuan Zhang and Kaiwen He and Chenri Luo and Jianxing Chen and Junwei Wu and Chen Xu and Ziyang Xu and Jimin Huang and Guojun Xiong and Xiao-Yang Liu and Qianqian Xie and Jian-Yun Nie},
journal= {arXiv preprint arXiv:2505.20650},
year = {2026}
}