Title: LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance Show Chinese title

Title: LicenseGPT：用于公开数据集许可合规的微调基础模型

Abstract: Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets. Ambiguities in dataset licenses pose significant legal risks, making it challenging even for software IP lawyers to accurately interpret rights and obligations. In this paper, we introduce LicenseGPT, a fine-tuned foundation model (FM) specifically designed for dataset license compliance analysis. We first evaluate existing legal FMs (i.e., FMs specialized in understanding and processing legal texts) and find that the best-performing model achieves a Prediction Agreement (PA) of only 43.75%. LicenseGPT, fine-tuned on a curated dataset of 500 licenses annotated by legal experts, significantly improves PA to 64.30%, outperforming both legal and general-purpose FMs. Through an A/B test and user study with software IP lawyers, we demonstrate that LicenseGPT reduces analysis time by 94.44%, from 108 seconds to 6 seconds per license, without compromising accuracy. Software IP lawyers perceive LicenseGPT as a valuable supplementary tool that enhances efficiency while acknowledging the need for human oversight in complex cases. Our work underscores the potential of specialized AI tools in legal practice and offers a publicly available resource for practitioners and researchers. Show Chinese abstract

Abstract: 数据集许可合规性是开发商业人工智能产品的关键但复杂的方面，尤其是在越来越多地使用公开可用数据集的情况下。 数据集许可中的模糊性带来了重大的法律风险，即使对于软件知识产权律师来说，准确解读权利和义务也极具挑战性。 在本文中，我们介绍了LicenseGPT，这是一种专门设计用于数据集许可合规性分析的微调基础模型（FM）。 我们首先评估现有的法律FM（即专门用于理解和处理法律文本的FM），发现表现最好的模型的预测一致性（PA）仅为43.75%。 LicenseGPT在由法律专家标注的500个许可的精选数据集上进行微调，显著提高了PA至64.30%，优于法律和通用FM。 通过与软件知识产权律师的A/B测试和用户研究，我们证明LicenseGPT将每份许可的分析时间从108秒减少到6秒，减少了94.44%，且未影响准确性。 软件知识产权律师认为LicenseGPT是一种有价值的补充工具，可以提高效率，同时承认复杂案件中需要人工监督。 我们的工作突显了专业AI工具在法律实践中潜力，并为从业人员和研究人员提供了一个公开可用的资源。