一句话：微调 LicenseGPT 用于公开数据集许可证合规，预测一致率升至 64.30%，律师分析时间减少 94.44%。
公开数据集许可证合规是开发商业 AI 产品的关键且复杂环节，许可证条款模糊带来法律风险。本文提出 LicenseGPT，一个面向数据集许可证合规分析的微调基础模型。作者先评估现有法律基础模型，最佳模型预测一致率（PA）仅 43.75%。随后在法律专家标注的 500 份许可证数据集上微调 LicenseGPT，使其 PA 提升至 64.30%，优于法律专用与通用基础模型。通过 A/B 测试和软件知识产权律师用户研究，LicenseGPT 将单份许可证分析时间从 108 秒降至 6 秒，减少 94.44%，且不牺牲准确率。律师认为其可作为有价值的辅助工具，但复杂案例仍需人工监督。该工作提供公开资源，并有望帮助 AI 软件开发者进行初步许可证检查，避免后期返工。
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. Moreover, LicenseGPT has the potential to assist AI software developers in managing preliminary license checks before involving legal counsel, helping to avoid costly late-stage rework and ensuring AI software compliance.
@inproceedings{paperbot9079,
title = {LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance},
author = {Jingwen Tan and Gopi Krishnan Rajbahadur and Zi Li and Xiangfu Song and Jianshan Lin and Dan Li and Zibin Zheng and Ahmed E. Hassan},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728530}
}