ACL

Memorization: A Close Look at Books

Iris Ma, Ian Domingo, Alberto Krone-Martins, Pierre Baldi, Cristina V. Lopes

机构 * School of Information and Computer Sciences University of California, Irvine（信息与计算机科学学院 加州大学伊维奇分校） 纠错

Comments Accepted at ACL 2025 L2M2 Workshop

IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction

Kaiyu He, Mian Zhang, Shuo Yan, Peilin Wu, Zhiyu Zoey Chen

机构 * Department of Computer Science University of Texas at Dallas（计算机科学系德克萨斯大学达拉斯分校） 纠错

Comments Accepted to ACL 2025 Findings

A Survey on Efficient Large Language Model Training: From Data-centric Perspectives

Junyu Luo, Bohan Wu, Xiao Luo, Zhiping Xiao, Yiqiao Jin, Rong-Cheng Tu, Nan Yin, Yifan Wang, Jingyang Yuan, Wei Ju, Ming Zhang

机构 * State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University（信息处理国家重点实验室，计算机学院，PKU-Anker LLM实验室，北京大学） ； University of California, Los Angeles（加州大学洛杉矶分校） ； University of Washington（华盛顿大学） ； Georgia Institute of Technology（佐治亚理工学院） ； Nanyang Technological University（南洋理工大学） ； HKUST（香港科技大学） ； University of International Business and Economics（国际商务经济大学） 纠错

Comments ACL 2025

BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models

Patrick Haller, Jonas Golde, Alan Akbik

机构 * Humboldt-Universität zu Berlin（柏林洪堡大学） 纠错

Comments 7 pages, 7 figures and tables, Published in Proceedings of the BabyLM Challenge 2025

Journal ref In Proceedings of the 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning, CoNLL 2024, pages 82 to 94, Miami, FL, USA. Association for Computational Linguistics