FinTagging
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FinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial Information
📄 Paper · 🤗 Collection · 💻 Code · 🌐 The Fin AI
Part of FinTagging — FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information (arXiv:2505.20650). Formerly
TheFinAI/FinTagging_BIO(the old name redirects here). A newer, larger version is available: en-fintagging-bio-v2.
FinTagging is 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. It decomposes the XBRL tagging problem into two subtasks:
- FinNI: Financial entity extraction.
- FinCL: Taxonomy-driven concept alignment.
FinTagging requires models to jointly extract facts from both unstructured text and structured tables and align them with the full 10k+ US-GAAP taxonomy.
This repository contains the original benchmark dataset without preprocessing. Annotated data (benchmark_ground_truth_pipeline.json) is provided in the "annotation" folder. For preprocessed datasets suitable for specific model architectures, please see the linked datasets in the Github README.
Datasets:
- FinNI-eval: Evaluation set for FinNI subtask.
- FinCL-eval: Evaluation set for FinCL subtask.
- FinTagging_BIO: BIO-format dataset for token-level tagging.
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