HORAE: A Domain-Agnostic Language for Automated Service Regulation
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913827626418176 |
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| author | Sun, Yutao Chen, Mingshuai Zhao, Tiancheng Zhao, Kangjia Li, He Chen, Jintao Wang, Zhongyi Lu, Liqiang Zhao, Xinkui Deng, Shuiguang Yin, Jianwei |
| author_facet | Sun, Yutao Chen, Mingshuai Zhao, Tiancheng Zhao, Kangjia Li, He Chen, Jintao Wang, Zhongyi Lu, Liqiang Zhao, Xinkui Deng, Shuiguang Yin, Jianwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HORAE: A Domain-Agnostic Language for Automated Service Regulation Sun, Yutao Chen, Mingshuai Zhao, Tiancheng Zhao, Kangjia Li, He Chen, Jintao Wang, Zhongyi Lu, Liqiang Zhao, Xinkui Deng, Shuiguang Yin, Jianwei Machine Learning Artificial Intelligence Computation and Language 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. |
| title | HORAE: A Domain-Agnostic Language for Automated Service Regulation |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2406.06600 |