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Talent Scout
Z
Zixun Lan
Department of Mechatronics and Robotics, Xi’an Jiaotong–Liverpool University
M
Maochun Xu
Department of Applied Mathematics, Xi’an Jiaotong–Liverpool University
Y
Yifan Ren
Department of Applied Mathematics, Xi’an Jiaotong–Liverpool University
R
Rui Wu
Hansheng Lawyers Building
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Founder's Pitch
"Specialized AI model optimized for Chinese labor law applications, enhancing legal practices' efficiency and accuracy."
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Why It Matters
The specialization of AI models in niche legal areas like labor law can significantly enhance the accuracy and reliability of legal processes, addressing complex tasks that general-purpose models handle inadequately. This has a pivotal role in increasing access to justice and improving operational efficiency within the legal sector.
Product Angle
Productize as a cloud-based AI service for legal firms and HR departments, providing modules for automated legal consulting, document generation, and real-time legal question answering, customized for the nuances of Chinese labor law.
Disruption
This solution could replace less efficient and costly manual legal research practices, reduce dependency on paralegal staff for routine tasks, and potentially reshape the way specialized legal consultations are conducted.
Product Opportunity
There is growing demand for AI solutions in the legal industry, driven by the need to improve efficiency and accuracy in legal processes. Companies, especially within China's vast market of corporate law and HR regulation, would pay for reliable, contextually accurate AI tools as a part of their compliance and risk management strategies.
Use Case Idea
A legal tech company developing an AI assistant focusing on labor law consultancy and documentation for businesses and legal firms, streamlining tasks like contract review, dispute resolution, and legal compliance audits.
Science
The paper introduces LabourLawLLM, a large language model specifically fine-tuned for Chinese labor law tasks. It includes a dedicated benchmark (LabourLawBench) to evaluate performance across tasks such as legal provision citation, question answering, and case classification. The methodology involves tailoring large language models through specialized training datasets and evaluation protocols, proving superior performance over existing general-purpose models.
Method & Eval
The solution's effectiveness is demonstrated by comparing LabourLawLLM's performance against both general-purpose and other legal-specific LLMs using a variety of evaluation metrics including ROUGE-L and accuracy. It shows consistent outperformance, highlighting its robustness and specialization in labor law tasks.
Caveats
The model is tailored specifically for Chinese labor law, limiting its application to different legal systems and domains. Continuous updates are necessary to include any changes in labor legislation, and its practical implementation could be hindered by legal restrictions on AI deployment in actual legal settings.
Author Intelligence
Zixun Lan
Department of Mechatronics and Robotics, Xi’an Jiaotong–Liverpool University
Maochun Xu
Department of Applied Mathematics, Xi’an Jiaotong–Liverpool University
Yifan Ren
Department of Applied Mathematics, Xi’an Jiaotong–Liverpool University
Rui Wu
Hansheng Lawyers Building
Jianghui Zhou
Suzhou Gewu Digital Technology Co., Ltd.
Xueyang Cheng
Kenneth Wang School of Law, Soochow University
Jian’an Ding
Kenneth Wang School of Law, Soochow University
Xinheng Wang
Department of Mechatronics and Robotics, Xi’an Jiaotong–Liverpool University
Mingmin Chi
College of Computer Science and Artificial Intelligence, Fudan University
Fei Ma
Department of Applied Mathematics, Xi’an Jiaotong–Liverpool University