FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verification
📰 ArXiv cs.AI
Learn to evaluate large language models for financial statement verification using FinVerBench, a benchmark for validity and calibration
Action Steps
- Build a financial statement verification model using a large language model
- Run FinVerBench on the model to evaluate its validity and calibration
- Configure the model to handle arithmetic, cross-statement linkage, year-over-year, and magnitude perturbations
- Test the model on SEC 10-K XBRL filings for S&P 500 companies
- Apply the four-category error taxonomy to identify and correct errors
Who Needs to Know This
Data scientists and AI engineers working on financial applications can use FinVerBench to assess the accuracy of their models, while product managers can utilize it to evaluate the reliability of financial statement verification tools
Key Insight
💡 FinVerBench provides a comprehensive framework for assessing the accuracy and reliability of large language models in financial statement verification
Share This
📊 Evaluate financial statement verification models with FinVerBench, a new benchmark for validity and calibration 📈
Key Takeaways
Learn to evaluate large language models for financial statement verification using FinVerBench, a benchmark for validity and calibration
Full Article
Title: FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verification
Abstract:
arXiv:2605.29586v1 Announce Type: new Abstract: We introduce FinVerBench, a benchmark and validity study for financial statement verification: determining whether a set of corporate financial statements is numerically consistent from the information shown to the model. FinVerBench is built from SEC 10-K XBRL filings for 43 S&P 500 companies and defines a four-category error taxonomy covering arithmetic, cross-statement linkage, year-over-year, and magnitude perturbations. We attempt fifteen cont
Abstract:
arXiv:2605.29586v1 Announce Type: new Abstract: We introduce FinVerBench, a benchmark and validity study for financial statement verification: determining whether a set of corporate financial statements is numerically consistent from the information shown to the model. FinVerBench is built from SEC 10-K XBRL filings for 43 S&P 500 companies and defines a four-category error taxonomy covering arithmetic, cross-statement linkage, year-over-year, and magnitude perturbations. We attempt fifteen cont
DeepCamp AI