Horae: A Domain-Agnostic Language for Automated Service Regulation

Yutao Sun, Mingshuai Chen, Tiancheng Zhao, Kangjia Zhao, He Li, Jintao Chen, Zhongyi Wang, Liqiang Lu, Xinkui Zhao, Shuiguang Deng, Jianwei Yin

Abstract: Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.

External IDs:dblp:conf/ijcai/SunCZZLCWLZDY25