RegGPT: A Tool for Cross-Domain Service Regulation Language Conversion

Zhaowen Wang, Qi Xie, Huan Zhang, Weihuan Min, Li Kuang, Lingyan Zhang

Abstract: Digital services have become essential to the modern service industry, offering great convenience to consumers. Because of the lack of regulation, it has posed an unprecedented challenge to the regulation of digital services. With the development of Regulatory Technology, many regulatory platforms have emerged. However, most of these platforms focus on a single service domain and are difficult to migrate to other domains to meet cross-domain regulatory demands. By extracting common elements from multi-domain regulatory rules, we propose a regulatory language called Cross-Domain Service Regulation Language (CDSRL), which aims to improve the comprehension of rules for machines, so the automated regulation can be achieved. Meanwhile, we construct the fine-tuned datasets for the regulatory domain and train the RegGPT based on the large language model, which can identify and classify natural language rules and automatically convert them into CDSRL. Experiments show that the language is highly comprehensible, scalable, and suitable for expressing rules in different domains and categories. The RegGPT shows a strong ability in the conversion process and improves regulatory efficiency. It provides a new scheme for automatically converting regulatory rule into rule language.