Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the lens of causality in order to model not only how various distributional factors associate with language change, but how they causally affect it. In particular, we study slang, which is an informal language that is typically restricted to a specific group or social setting. We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words. With causal discovery and causal inference techniques, we measure the effect that word type (slang/nonslang) has on both semantic change and frequency shift, as well as its relationship to frequency, polysemy and part of speech. Our analysis provides some new insights in the study of language change, e.g., we show that slang words undergo less semantic change but tend to have larger frequency shifts over time.
SLANG: New Concept Comprehension of Large Language Models
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Merced（加利福尼亚大学默塞德分校）；University of Chinese Academy of Sciences（中国科学院大学）
A Framework for Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base
Covenant University；Federal University Lokoja（洛科贾联邦大学）；Cape Peninsula University of Technology（开普半岛理工大学）；Landmark University（兰马克大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）
The Causal Influence of Grammatical Gender on Distributional Semantics
Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；ETH Zurich（苏黎世联邦理工学院）；FAIR at Meta AI（Meta AI 旗下 FAIR 实验室）；University of Copenhagen（哥本哈根大学）
Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei
School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）；SKLPlanets, Macau University of Science and Technology（澳门科技大学SKLPlanets）；College of Software, Northeastern University（东北大学软件学院）；School of Energy and Power Engineering, Huazhong University of Science and Technology（华中科技大学能源与动力工程学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
ReBeCA: Causality-Inspired Behavior Analysis for Understanding the Self-Reflection of Language Models
Monash University（莫纳什大学）；Harbin Institute of Technology（哈尔滨工业大学）；Hainan University（海南大学）；South China University of Technology（华南理工大学）；The Chinese University of Hong Kong（香港中文大学）
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
TomTom（TomTom公司）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Saarland Informatics Campus（萨尔信息学园区）
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective
Fudan University（复旦大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhejiang University（浙江大学）；Renmin University of China（中国人民大学）；Alibaba Group（阿里巴巴集团）
