Knowledge Commons of Institute of Automation,CAS
Parallel Learning for Legal Intelligence: A HANOI Approach Based on Unified Prompting | |
Song, Zhuoyang1; Huang, Min1; Miao, Qinghai1; Wang, Fei-Yue2 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS |
ISSN | 2329-924X |
2023-08-14 | |
页码 | 11 |
通讯作者 | Miao, Qinghai(miaoqh@ucas.ac.cn) |
摘要 | Pretrained language models (PLMs) have made significant progress on various NLP tasks recently. However, PLMs encounter challenges when it comes to domain-specific tasks such as legal AI. These tasks often involve intricate expertise, expensive data annotation, and limited training data availability. To tackle this problem, we propose a human-oriented artificial-natural parallel system for organized intelligence (HANOI)-Legal based on the parallel learning (PL) framework. First, by regarding the description in PL as the pretraining process based on a large-scale corpus, we setup an artificial system based on a PLM. Second, to adapt the PLM to legal tasks with limited resources, we propose UniPrompt as a prescription. UniPrompt serves as a unified prompt-based training framework, enabling the utilization of diverse open datasets for these tasks. Third, we labeled a few task-specific legal data through distributed autonomous operations (DAO-II) for further fine-tuning. By combining a scalable unified-task-format reformulation and a unified-prompt-based training pipeline, HANOI-Legal leverages PLMs' linguistic capabilities acquired from a variety of open datasets to generate task-specific models. Our experiments in two legal domain tasks show that HANOI-Legal achieved an excellent performance in low-resource scenarios compared to the state-of-the-art prompt-based approach. |
关键词 | Index Ternis- Natural language processing (NLP) parallel learning (PL) parallel systems pretrained language model (PLM) prompt tuning |
DOI | 10.1109/TCSS.2023.3301400 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020YFB2104001] ; National Natural Science Foundation of China[62271485] ; Open Project of the State Key Laboratory for Management and Control of Complex Systems[20220117] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Open Project of the State Key Laboratory for Management and Control of Complex Systems |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Cybernetics ; Computer Science, Information Systems |
WOS记录号 | WOS:001051270900001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54018 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Miao, Qinghai |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Song, Zhuoyang,Huang, Min,Miao, Qinghai,et al. Parallel Learning for Legal Intelligence: A HANOI Approach Based on Unified Prompting[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:11. |
APA | Song, Zhuoyang,Huang, Min,Miao, Qinghai,&Wang, Fei-Yue.(2023).Parallel Learning for Legal Intelligence: A HANOI Approach Based on Unified Prompting.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,11. |
MLA | Song, Zhuoyang,et al."Parallel Learning for Legal Intelligence: A HANOI Approach Based on Unified Prompting".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):11. |
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