EMNLP

Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models

儿童导向语言并不总是能提升语言模型的语法学习

Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, Arianna Bisazza

机构 * Center for Language and Cognition (CLCG) University of Groningen（语言与认知中心（CLCG）格罗宁根大学） 纠错

AI总结 该研究发现儿童导向语言在提升语言模型语法学习方面效果不一致，且维基百科数据表现更优，提出FIT-CLAMS方法以更公平评估语法能力。

Comments 21 pages, 4 figures, 4 tables

Journal ref Proceedings of EMNLP 2025

Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages

对词汇规范化进行综合评估：面向无分隔语言的边界感知方法

Shohei Higashiyama, Masao Utiyama

机构 * National Institute of Information and Communications Technology（信息与通信技术国家研究所） 纠错

AI总结 本文针对无分隔语言提出词汇规范化方法，构建大规模数据集并验证了编码器和解码器在多视角评估中的有效性。

Comments EMNLP 2025 (Findings), 26 pages

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents

Collab-Overcooked: 大语言模型作为协作代理的基准测试与评估

Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang

机构 * Beijing University of Posts and Telecommunications（北京邮电大学） ； Li Auto Inc 纠错

AI总结 本文提出Collab-Overcooked基准测试，评估大语言模型在协作代理中的表现，揭示其在目标解释和协作能力上的优劣。

Comments Accepted to EMNLP 2025 Main Conference. Camera-Ready Version. 30 pages, 17 figures

Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition

Whispering LLaMA: 一种用于语音识别的跨模态生成性错误校正框架

Srijith Radhakrishnan, Chao-Han Huck Yang, Sumeer Ahmad Khan, Rohit Kumar, Narsis A. Kiani, David Gomez-Cabrero, Jesper N. Tegner

AI总结 Whispering LLaMA通过跨模态融合技术提升语音识别的生成性错误校正性能，相较n-best假设提升37.66%的词错误率。

Comments Accepted to EMNLP 2023 as main paper. 10 pages. Revised math notations. GitHub: https://github.com/Srijith-rkr/Whispering-LLaMA