Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training processes that models typically undergo-such as supervised fine-tuning, extensive continued pretraining, reinforcement learning, multi-modal extension, pruning, and upcycling-pose significant challenges to reliable identification. In this work, we propose a training-free fingerprinting method based on weight matrices. We leverage the Linear Assignment Problem (LAP) and an unbiased Centered Kernel Alignment (CKA) similarity to neutralize the effects of parameter manipulations, yielding a highly robust and high-fidelity similarity metric. On a comprehensive testbed of 60 positive and 90 negative model pairs, our method demonstrates exceptional robustness against all six aforementioned post-training categories while exhibiting a near-zero risk of false positives. By achieving perfect scores on all classification metrics, our approach establishes a strong basis for reliable model lineage verification. Moreover, the entire computation completes within 30s on an NVIDIA 3090 GPU. The code is available at https://github.com/LUMIA-Group/AWM.

翻译：鉴于大语言模型训练所需的大量资源，保护其知识产权至关重要。因此，模型所有者和第三方都迫切需要判断一个可疑的大语言模型是独立从头训练所得，还是基于现有基础模型衍生而来。然而，模型通常经历密集的后训练过程——例如监督微调、大规模持续预训练、强化学习、多模态扩展、剪枝与升级利用——这些过程对可靠识别构成了重大挑战。本研究提出一种基于权重矩阵的无训练指纹提取方法。我们利用线性分配问题与无偏中心核对齐相似度来消除参数操作的影响，从而得到一种高度鲁棒且高保真的相似性度量。在一个包含60组正样本对与90组负样本对的综合测试平台上，我们的方法在面对全部六类后训练操作时均表现出卓越的鲁棒性，同时展现出接近零的误报风险。通过在所有分类指标上获得满分表现，本方法为可靠的模型谱系验证奠定了坚实基础。此外，整个计算过程在NVIDIA 3090 GPU上可在30秒内完成。代码已发布于https://github.com/LUMIA-Group/AWM。