title: FinTagging: An LLM-ready Benchmark for Extracting and Structuring
    Financial Information
summary: 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.
paper_authors._id: 68376120fa4820e924fd2b35
paper_authors.name: Yan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T20:10:14.599Z
paper_authors._id: 65d76cc5b9b7b8bf88faa916
paper_authors.avatarUrl: /avatars/d95232cd0c307efab6197ade1a66190b.svg
paper_authors.fullname: Yan Wang
paper_authors.type: user
paper_authors.user: YanAdjeNole
paper_authors._id: 68376120fa4820e924fd2b36
paper_authors.name: Yang Ren
paper_authors._id: 68376120fa4820e924fd2b37
paper_authors.name: Lingfei Qian
paper_authors._id: 68376120fa4820e924fd2b38
paper_authors.name: Xueqing Peng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:50:54.123Z
paper_authors._id: 63a0c0803c8841cfe2cd1f15
paper_authors.avatarUrl: /avatars/bbe216db7a33612f23d23ce4ed4ba3f9.svg
paper_authors.fullname: Xueqing Peng
paper_authors.type: user
paper_authors.user: Xueqing
paper_authors._id: 68376120fa4820e924fd2b39
paper_authors.name: Keyi Wang
paper_authors._id: 68376120fa4820e924fd2b3a
paper_authors.name: Yi Han
paper_authors._id: 68376120fa4820e924fd2b3b
paper_authors.name: Dongji Feng
paper_authors._id: 68376120fa4820e924fd2b3c
paper_authors.name: Xiao-Yang Liu
paper_authors._id: 68376120fa4820e924fd2b3d
paper_authors.name: Jimin Huang
paper_authors._id: 68376120fa4820e924fd2b3e
paper_authors.name: Qianqian Xie
paper_title: FinTagging: An LLM-ready Benchmark for Extracting and Structuring
    Financial Information
paper_summary: 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.
paper_ai_summary: FinTagging evaluates LLMs for structured information extraction and semantic alignment in XBRL financial reporting, revealing challenges in fine-grained concept alignment.
paper_ai_keywords: FinTagging XBRL structured information extraction semantic alignment large language models FinNI FinCL US-GAAP taxonomy zero-shot settings