The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indistinguishable from human-written texts. Recent research has predominantly focused on using LLMs to classify text as either human-written or machine-generated. In our study, we adopt a different approach by profiling texts spanning four domains based on 250 distinct linguistic features. We select the M4 dataset from the Subtask B of SemEval 2024 Task 8. We automatically calculate various linguistic features with the LFTK tool and additionally measure the average syntactic depth, semantic similarity, and emotional content for each document. We then apply a two-dimensional PCA reduction to all the calculated features. Our analyses reveal significant differences between human-written texts and those generated by LLMs, particularly in the variability of these features, which we find to be considerably higher in human-written texts. This discrepancy is especially evident in text genres with less rigid linguistic style constraints. Our findings indicate that humans write texts that are less cognitively demanding, with higher semantic content, and richer emotional content compared to texts generated by LLMs. These insights underscore the need for incorporating meaningful linguistic features to enhance the understanding of textual outputs of LLMs.
Cross-linguistic disagreement as a conflict of semantic alignment norms in multilingual AI~Linguistic Diversity as a Problem for Philosophy, Cognitive Science, and AI~
Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；Victoria University of Wellington（惠灵顿维多利亚大学）；Macquarie University（麦考瑞大学）；Nanzan University（南山大学）；Kyoto University（京都大学）
School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；City University of Hong Kong（香港城市大学）；Shanghai Jiao Tong University（上海交通大学）；University of Oxford, Department of Engineering Science（牛津大学工程科学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；National Institute of Standards and Technology（美国国家标准与技术研究院）；INSAIT（保加利亚索菲亚大学INSAIT研究院）；Sofia University “St. Kliment Ohridski”（圣基里曼·奥赫里德斯基索菲亚大学）
Cross-Lingual Consistency: A Novel Inference Framework for Advancing Reasoning in Large Language Models
Shandong Inspur Artificial Intelligence Research Institute Co., Ltd.（山东浪潮人工智能研究院有限公司）；Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Center Co., Ltd.（山东云海国创云计算装备产业创新中心有限公司）
MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies
Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Technical University of Berlin（柏林工业大学）
Are LLMs Stable Formal Logic Translators in Logical Reasoning Across Linguistically Diversified Texts?
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）
Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan
Bavarian Academy of Sciences（巴伐利亚科学院）；LMU Munich（慕尼黑大学）；Albert-Ludwigs-Universität Freiburg（弗莱堡大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
KGQuest: Template-Driven QA Generation from Knowledge Graphs with LLM-Based Refinement
Scuola Superiore Sant’Anna, Pisa, Italy（圣安娜高等学院）；Sapienza University of Rome, Rome, Italy（罗马萨皮恩扎大学）；Institute of Informatics and Telematics, National Research Council of Italy (CNR)（意大利信息与电信研究所，国家研究理事会（CNR））
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages
Nanyang Technological University（南洋理工大学）；Southwest Jiaotong University（西南交通大学）；Kash Institute of Electronics and Information Industry（Kash电子信息产业研究院）；Southwest Petroleum University（西南石油大学）
An Empirical Exploration of ChatGPT's Ability to Support Problem Formulation Tasks for Mission Engineering and a Documentation of its Performance Variability
University of Southern California（南加州大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Virginia Tech（弗吉尼亚理工大学）
本文通过 NASA 太空任务设计挑战实证评估 ChatGPT-3.5 在任务工程问题表述中识别利益相关者的能力，发现其对人本利益相关者识别较好但对环境因素识别差、抽象层次保持不足且并行输出变异性大，表明其可靠性有限。
