通过黑盒检测输出相似性，可高精度识别大模型是否源自其他模型。
Model Provenance Testing for Large Language Models
- 基于输出统计相似性，用多重假设检验比对模型间关系。
- 在600多个模型上实现90%以上精确率与80%以上召回率。
- 适合需要溯源版权或排查模型风险的开发者和机构使用。
大型语言模型通过微调等手段不断被定制化，给版权保护和下游影响管理带来挑战。追踪模型来源对于维护知识产权、发现基础模型中的偏见或漏洞后定位衍生模型至关重要。本文提出一种模型溯源测试框架，用于判断一个模型是否由另一个模型衍生而来。核心思路是：真实场景下的模型衍生会保留显著的输出相似性，可通过统计方法检测。仅需黑盒访问（如API），我们采用多重假设检验，将待测模型与无关联模型建立的基准进行对比。在涵盖30M至4B参数、超600个模型的两个真实世界基准上，该方法实现90-95%精确率和80-90%召回率，证明了在仅有API访问条件下，系统化溯源验证在生产环境中的可行性。
原文摘要 · Abstract (English)
Large language models are increasingly customized through fine-tuning and other adaptations, creating challenges in enforcing licensing terms and managing downstream impacts. Tracking model origins is crucial both for protecting intellectual property and for identifying derived models when biases or vulnerabilities are discovered in foundation models. We address this challenge by developing a framework for testing model provenance: Whether one model is derived from another. Our approach is based on the key observation that real-world model derivations preserve significant similarities in model outputs that can be detected through statistical analysis. Using only black-box access to models, we employ multiple hypothesis testing to compare model similarities against a baseline established by unrelated models. On two comprehensive real-world benchmarks spanning models from 30M to 4B parameters and comprising over 600 models, our tester achieves 90-95% precision and 80-90% recall in identifying derived models. These results demonstrate the viability of systematic provenance verification in production environments even when only API access is available.
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