EMNLP

ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks

Haroun Elleuch, Youssef Saidi, Salima Mdhaffar, Yannick Estève, Fethi Bougares

Comments Published in Proceedings of the ArabicNLP 2025 Workshop (co-located with EMNLP 2025), Association for Computational Linguistics, 2025

From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling

Mohsinul Kabir, Tasfia Tahsin, Sophia Ananiadou

机构 * Department of Computer Science, National Center for Text Mining, The University of Manchester（计算机科学系，文本挖掘国家中心，曼彻斯特大学） ； Department of Computer Science and Engineering, Islamic University of Technology（计算机科学与工程系，伊斯兰技术大学） 纠错

Comments Accepted at EMNLP 2025 (Findings)

Journal ref Findings of the Association for Computational Linguistics: EMNLP 2025

Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou

机构 * Department of Computer Science, National Center for Text Mining, The University of Manchester（计算机科学系，文本挖掘国家中心，曼彻斯特大学） ； Department of Computer Science and Engineering, Islamic University of Technology（计算机科学与工程系，伊斯兰技术大学） 纠错

Comments Accepted at EMNLP 2025 (Main)

Journal ref Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning

Jianfeng Pan, Senyou Deng, Shaomang Huang

机构 * Security Technology Inc.（360安全技术公司） 纠错

Comments 18 pages, 10 figures, Accepted by EMNLP 2025 (Findings)

From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing

Lanxiao Huang, Daksh Dave, Tyler Cody, Peter Beling, Ming Jin

机构 * Bradley Department of Electrical and Computer Engineering, Virginia Tech（弗吉尼亚理工大学电气与计算机工程系布雷拉德部门） ； National Security Institute, Virginia Tech（弗吉尼亚理工大学国家安全研究所） 纠错

Journal ref Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 15890 to 15916, Suzhou, China, November 2025

Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation

Daniel Schwartz, Dmitriy Bespalov, Zhe Wang, Ninad Kulkarni, Yanjun Qi

机构 * Amazon Bedrock Science（亚马逊Bedrock科学） ； Drexel University（德雷塞尔大学） ； University of Virginia（弗吉尼亚大学） 纠错

Comments 14 pages, 5 figures; published in EMNLP 2025 ; Code at: https://github.com/dsbuddy/GAP-LLM-Safety