NLP Field
Natural Language Processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. Current research emphasizes improving model performance across diverse tasks, including question answering, text classification, and information extraction, often leveraging large language models (LLMs) and transformer architectures. These advancements are significantly impacting various fields, from healthcare (e.g., dementia detection, clinical data analysis) and legal (e.g., document processing, legal reasoning) to education and cybersecurity, by automating tasks and providing new analytical capabilities. A key challenge remains ensuring fairness, mitigating biases, and addressing privacy concerns within these powerful models.
Papers
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
Oleksandr Yermilov, Vipul Raheja, Artem Chernodub
The economic trade-offs of large language models: A case study
Kristen Howell, Gwen Christian, Pavel Fomitchov, Gitit Kehat, Julianne Marzulla, Leanne Rolston, Jadin Tredup, Ilana Zimmerman, Ethan Selfridge, Joseph Bradley
TalkUp: Paving the Way for Understanding Empowering Language
Lucille Njoo, Chan Young Park, Octavia Stappart, Marvin Thielk, Yi Chu, Yulia Tsvetkov
LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages
Milind Agarwal, Md Mahfuz Ibn Alam, Antonios Anastasopoulos
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, Jie Fu
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
Aniket Pramanick, Yufang Hou, Saif M. Mohammad, Iryna Gurevych
This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models
Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett