印度司法背景下的法律问答系统的数据集
Veningston K1, Apratim Mishra1
1Department of Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir 190006, India.
Data in brief
|June 16, 2025
概括
这项研究引入了印度法律问答的新数据集,其中包括来自最高法院判决的10,000对问答对. 这个资源旨在改善AI驱动的复杂印度法律的法律信息检索.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 法律信息学 法律信息学
背景情况:
- 法律文件是复杂的,难以搜索.
- 现有的法律问题答案 (LQA) 系统缺乏针对印度等多样化的司法系统的专业数据集.
研究的目的:
- 为印度司法机构量身定制的法律问题答案 (LQA) 提供一个全面的数据集.
- 为了促进有效的法律信息检索和支持AI驱动的LQA系统的发展.
主要方法:
- 从1256个印度最高法院判决中创建了一个由10,000个问答对组成的数据集.
- 该数据集涵盖民法和刑事法领域,并提供相关的元数据.
- 在Llama-2-7b-hf模型上,IndicLegalQA数据集使用参数有效微调 (PEFT) 与低级调整 (LoRA) 进行了微调.
- 模型评估使用 Sentence-BERT (SBERT) 和等号相似度来评估答案的准确性.
主要成果:
- 开发的数据集使人工智能系统能够根据印度最高法院的判决回答法律问题.
- 微调证明了数据集适用于LQA任务的适用性,用等号相似度测量性能.
- 印度法律QA数据集支持人工智能驱动的印度法律法律信息检索.
结论:
- IndicLegalQA数据集是推动AI在法律领域的宝贵资源,特别是在印度司法部门.
- 它提高了获得准确法律信息的机会,帮助法律专业人士和公民.
- 该数据集有助于开发更有效的法律问答系统.
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