FinTagging: An LLM-ready Benchmark for Extracting and Structuring
  Financial Information
Authors: name: Yan Wang, name: Yang Ren, name: Lingfei Qian, name: Xueqing Peng, name: Keyi Wang, name: Yi Han, name: Dongji Feng, name: Xiao-Yang Liu, name: Jimin Huang, name: Qianqian Xie
Organization: The Fin AI

We introduce FinTagging, the first full-scope, table-aware XBRL benchmark
designed to evaluate the structured information extraction and semantic
alignment capabilities of large language models (LLMs) in the context of
XBRL-based financial reporting. Unlike prior benchmarks that oversimplify XBRL
tagging as flat multi-class classification and focus solely on narrative text,
FinTagging decomposes the XBRL tagging problem into two subtasks: FinNI for
financial entity extraction and FinCL for taxonomy-driven concept alignment. It
requires models to jointly extract facts and align them with the full 10k+
US-GAAP taxonomy across both unstructured text and structured tables, enabling
realistic, fine-grained evaluation. We assess a diverse set of LLMs under
zero-shot settings, systematically analyzing their performance on both subtasks
and overall tagging accuracy. Our results reveal that, while LLMs demonstrate
strong generalization in information extraction, they struggle with
fine-grained concept alignment, particularly in disambiguating closely related
taxonomy entries. These findings highlight the limitations of existing LLMs in
fully automating XBRL tagging and underscore the need for improved semantic
reasoning and schema-aware modeling to meet the demands of accurate financial
disclosure. Code is available at our GitHub repository and data is at our
Hugging Face repository.