Abstract
The process of mapping products to jurisdiction-specific tax categories and validating rates against evolving legislation can involve complex, manual effort. A system is described that may leverage Large Language Models (LLMs) to assist with these tasks. The system can be configured to perform functions such as interpreting natural-language text from tax laws to classify products from an internal catalog. Additionally, the system can source and ingest public tax data to compare a company's internal tax rule configurations against external information sources. By automating the cross-referencing of internal product data with external legal and rate information, such a system can facilitate scalable validation of tax rules across multiple jurisdictions. This approach may help manage compliance risks and reduce reliance on certain resource-intensive manual review processes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Chandna, Sandeep and Narinsky, Misha, "Tax Rule Validation Using Large Language Models", Technical Disclosure Commons, (August 20, 2025)
https://www.tdcommons.org/dpubs_series/8488