FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark
  for Evaluating LLMs
Authors: name: Yan Wang, name: Keyi Wang, name: Shanshan Yang, name: Jaisal Patel, name: Jeff Zhao, name: Fengran Mo, name: Xueqing Peng, name: Lingfei Qian, name: Jimin Huang, name: Guojun Xiong, name: Xiao-Yang Liu, name: Jian-Yun Nie
Organization: The Fin AI

The complexity of the Generally Accepted Accounting Principles (GAAP) and the
hierarchical structure of eXtensible Business Reporting Language (XBRL) filings
make financial auditing increasingly difficult to automate and verify. While
large language models (LLMs) have demonstrated strong capabilities in
unstructured text understanding, their ability to reason over structured,
interdependent, and taxonomy-driven financial documents remains largely
unexplored. To fill this gap, we introduce FinAuditing, the first
taxonomy-aligned, structure-aware, multi-document benchmark for evaluating LLMs
on financial auditing tasks. Built from real US-GAAP-compliant XBRL filings,
FinAuditing defines three complementary subtasks, FinSM for semantic
consistency, FinRE for relational consistency, and FinMR for numerical
consistency, each targeting a distinct aspect of structured auditing reasoning.
We further propose a unified evaluation framework integrating retrieval,
classification, and reasoning metrics across these subtasks. Extensive
zero-shot experiments on 13 state-of-the-art LLMs reveal that current models
perform inconsistently across semantic, relational, and mathematical
dimensions, with accuracy drops of up to 60-90% when reasoning over
hierarchical multi-document structures. Our findings expose the systematic
limitations of modern LLMs in taxonomy-grounded financial reasoning and
establish FinAuditing as a foundation for developing trustworthy,
structure-aware, and regulation-aligned financial intelligence systems. The
benchmark dataset is available at Hugging Face.