FinTagging: An LLM-ready Benchmark for Extracting and Structuring
  Financial Information
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.
68376120fa4820e924fd2b35
Yan Wang
claimed_verified
2025-05-28T20:10:14.599Z
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Yan Wang
user
YanAdjeNole
68376120fa4820e924fd2b36
Yang Ren
68376120fa4820e924fd2b37
Lingfei Qian
claimed_verified
2025-06-11T20:21:23.497Z
65d50fd888f4a90589017398
/avatars/5e58259399ffe0d4e2e5a8c81fe65b30.svg
Lingfei Qian
user
lfqian
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Xueqing Peng
claimed_verified
2025-06-03T08:50:54.123Z
63a0c0803c8841cfe2cd1f15
/avatars/bbe216db7a33612f23d23ce4ed4ba3f9.svg
Xueqing Peng
user
Xueqing
68376120fa4820e924fd2b39
Keyi Wang
68376120fa4820e924fd2b3a
Yi Han
68376120fa4820e924fd2b3b
Dongji Feng
68376120fa4820e924fd2b3c
Xiao-Yang Liu
68376120fa4820e924fd2b3d
Jimin Huang
claimed_verified
2025-06-19T09:10:50.414Z
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Jimin Huang
user
jiminHuang
68376120fa4820e924fd2b3e
Qianqian Xie
FinTagging: An LLM-ready Benchmark for Extracting and Structuring
  Financial Information
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.
FinTagging evaluates LLMs for structured information extraction and semantic alignment in XBRL financial reporting, revealing challenges in fine-grained concept alignment.
FinTagging XBRL structured information extraction semantic alignment large language models FinNI FinCL US-GAAP taxonomy zero-shot settings