arXiv:2608. 07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships that reflect model origin, ownership, and evolution.
By Yiwei Chen, Bingqi Shang, Sijia Liu
arXiv:2608. 08139v1 Announce Type: new Abstract: Establishing the provenance of a language model---including its base checkpoint and possible overlap in training distributions---is a governance challenge that metadata alone cannot resolve.
By Yuqi Wu, Shengming Zhao, Jie Chen
arXiv:2505. 12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification.
By Yun-Yun Tsai, Jia Hao Liang, Chuan Guo, Junfeng Yang, Laurens van der Maaten
arXiv:2609.36569v1 Announce Type: cross
Abstract: Checkpoint selection is a routine decision in supervised fine-tuning (SFT): training produces multiple checkpoints, but only one is retained. Yet fix...
By Yupeng Chang, Wenxuan Zhang, Yuan Wu
arXiv:2607. 10617v1 Announce Type: new Abstract: The lineage graph of open-weight language models is self-reported: Hugging Face's base_model metadata field is optional and unverified, and over 60% of Hub models document no parentage at all.
By Muhammad Awais Bin Adil, Saad Aamir
Measuring training data influence consistently across language model pretraining is challenging. It is difficult to select downstream tasks or validation sets representative of a model's general capabilities, and reliance on task performance at intermediate checkpoints complicates comparisons across training.