LakeHopper is a method for adapting column type annotators (CTA) from one data lake to another by treating cross‑lake adaptation as a knowledge‑management problem. It decomposes the source annotator’s knowledge into source‑specific, shared, and target‑specific parts, and then uses three mechanisms—label‑set realignment, LLM‑verified gap discovery, and cluster‑based propagation with rehearsal fine‑tuning—to adapt the annotator under a limited annotation budget. The approach achieves up to a 71.4% relative macro‑F1 improvement over three PLM backbones, reaches near‑full data quality with less than 6% of target labels, and trains 27–131 times faster than fine‑tuned table LLMs.
By Yushi Sun, Xujia Li, Nan Tang, Quanqing Xu, Chuanhui Yang, Lei Chen
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
By Xinlong Zhao, Dongsheng Liu, Hengyu Zhao, Zixuan Fu, Zheng Wang, Jie Cai, Jie Zhou, Qiang Ma, Xuanhe Zhou, Xu Han, Yudong Wang, Zhiyuan Liu
arXiv:2608. 11022v1 Announce Type: cross Abstract: Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations, and intended use.
By Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji, Rafael Ferreira da Silva, Wesley Brewer, Valentine Anantharaj, Sandro Fiore, Renan Souza
arXiv:2604. 01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.
By Muxing Li, Zesheng Ye, Sharon Li, Feng Liu
arXiv:2607. 13037v1 Announce Type: new Abstract: When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which training records belong to a given author.
By Haolin Xue
arXiv:2608.29966v1 Announce Type: new
Abstract: Domain adaptation of large language models increasingly depends on constructing high-quality training data, yet existing data-preparation pipelines typ...
By Cehao Yang, Xiaojun Wu, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Hui Xiong, Jian Guo