ACL

Sample-Efficient Language Modeling with Linear Attention and Lightweight Enhancements

Patrick Haller, Jonas Golde, Alan Akbik

机构 * Humboldt-Universität zu Berlin（洪堡大学柏林分校） 纠错

Journal ref Proceedings of the First BabyLM Workshop 2025, pages 175 to 191, Suzhou, China. Association for Computational Linguistics

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond

Yinghao Hu, Yaoyao Yu, Leilei Gan, Bin Wei, Kun Kuang, Fei Wu

机构 * College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院） ； Guanghua Law School, Zhejiang University（浙江大学光华法学院） ； School of Software Technology, Zhejiang University（浙江大学软件学院） ； Law & AI Lab, Zhejiang University（浙江大学法律与人工智能实验室） ； Shanghai AI Laboratory（上海人工智能实验室） 纠错

Comments 23 pages, Published in Findings of the Association for Computational Linguistics: EMNLP 2025

Likelihood-based Mitigation of Evaluation Bias in Large Language Models

Masanari Oi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, Naoaki Okazaki

机构 * Tokyo Institute of Technology（东京技术大学） ； MBZUAI 纠错

Comments 5 main pages

Journal ref ACL2024 (findings)

Learning Task Representations from In-Context Learning

Baturay Saglam, Xinyang Hu, Zhuoran Yang, Dionysis Kalogerias, Amin Karbasi

机构 * Yale University（耶鲁大学） 纠错

Comments ACL Findings 2025