Natural language artefact descriptions are primary carriers of engineering design knowledge, whose retrieval, representation, and reuse are fundamental to supporting knowledge-intensive tasks in the design process. In this paper, we explicate design knowledge from patented artefact descriptions as knowledge graphs and examine these to understand the linguistic and structural basis. The purpose of our work is to advance the traditional and ontological perspectives of design knowledge and to guide Large-Language Models (LLMs) on how to articulate natural language responses that reflect knowledge that is valuable in a design environment. We populate 33,881 knowledge graphs from a sample of patents stratified according to technology classes. For linguistic basis, we conduct Zipf distribution analyses on the frequencies of unique entities and relationships to identify 64 and 37 generalisable linguistic syntaxes respectively. The relationships largely represent attributes ('of'), structure ('in', 'with'), purpose ('to', 'for'), hierarchy ('include'), exemplification ('such as'), and behaviour ('to', 'from'). For structural basis, we draw inspiration from various studies on biological/ecological networks and discover motifs from patent knowledge graphs. We identify four 3-node and four 4-node subgraph patterns that could be converged and simplified into sequence [->...->], aggregation [->...<-], and hierarchy [<-...->]. Based on these results, we suggest concretisation strategies for entities and relationships and explicating hierarchical structures, potentially aiding the construction and modularisation of design knowledge.
Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency
Mozilla Foundation（Mozilla基金会）；School of Computer Science and Statistics（计算机科学与统计学院）；Trinity College Dublin（都柏林三一学院）；Department of Psychology（心理学系）；Mary Immaculate College（圣玛丽学院）
A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）
Linguistics and Human Brain: A Perspective of Computational Neuroscience
Institute of AI and Robotics, College of Intelligent Robotics and Advanced Manufacturing, Fudan University（人工智能与机器人研究院，智能机器人与先进制造学院，复旦大学）；Department of Language Science and Technology, The Hong Kong Polytechnic University（语言科学与技术系，香港理工大学）；Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
Brigham Young University（杨百翰大学）
预先训练的语言模型从它们所训练的大量语料库中获得大量的语言和事实知识，并且提示工程试图将这些模型与特定任务相匹配。不幸的是，现有的提示工程方法需要大量的标记数据，访问模型参数，或两者兼而有之。本文介绍了一种新的选择提示模板\textit{without labeled examples}和\textit{without direct access to the model}的方法。具体来说，在一组候选模板，我们选择的模板，最大化的互信息之间的输入和相应的模型输出。在8个数据集，代表7个不同的NLP任务，我们表明，当一个模板具有高互信息，它也有高精度的任务。在最大的模型上，使用我们的方法选择提示可以获得从平均提示准确度到最佳提示准确度的90%，并且不需要真值标签。
The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
East China Normal University（东华大学）；Information Engineering University（信息工程大学）；Zhengzhou University（郑州大学）
A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning
Idiap Research Institute（Idiap研究 institute）；École Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；Honda Research Institute Europe（本田欧洲研究院）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；National Biomarker Centre, CRUK-MI, University of Manchester（曼彻斯特大学国家生物标记中心）
Winchester School of Art, University of Southampton（南安普顿大学温彻斯特艺术学院）；Institute for Culture and Society, Western Sydney University（西悉尼大学文化与社会研究所）；Toronto Metropolitan University（多伦多都会大学）
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality
Shenzhen Technology University（深圳技术大学）；The Hong Kong Polytechnic University（香港理工大学）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）；HKUST (Guangzhou)（香港科技大学（广州））
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text
Technological University Dublin（都柏林理工大学）；SFI Centre for Research Training in Machine Learning（SFI机器学习研究培训中心）；School of Computer Science（计算机学院）；Artificial Intelligence and Cognitive Load Research Lab（人工智能与认知负荷研究实验室）
False perspectives on human language: why statistics needs linguistics
University School for Advanced Studies IUSS Pavia（帕维亚高等研究院大学）；Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；University of Geneva（日内瓦大学）；Yale University（耶鲁大学）
Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge
Institute for Human and Machine Cognition (IHMC)（人类与机器认知研究所（IHMC））；State University of New York, Albany（纽约州立大学奥尔巴尼分校）；University of North Carolina, Charlotte（北卡罗来纳大学夏洛特分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
