Large Language Models Can Better Understand Knowledge Graphs Than We Thought
Southeast University（东南大学）；The University of New South Wales（新南威尔士大学）；Monash University（莫纳什大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Xinbang Dai, Yuncheng Hua, Tongtong Wu, Yang Sheng, Qiu Ji, Guilin Qi
When we integrate factual knowledge from knowledge graphs (KGs) into large language models (LLMs) to enhance their performance, the cost of injection through training increases with the scale of the models. Consequently, there is significant interest in developing prompt strategies that effectively incorporate KG information into LLMs. However, the community has not yet comprehensively understood how LLMs process and interpret KG information in different input formats and organizations within prompts, and researchers often rely on trial and error. To address this gap, we design extensive experiments to empirically study LLMs' comprehension of different KG prompts. At the literal level, we reveal LLMs' preferences for various input formats (from linearized triples to fluent natural language text). At the attention distribution level, we discuss the underlying mechanisms driving these preferences. We then investigate how the organization of structured knowledge impacts LLMs and evaluate LLMs' robustness in processing and utilizing KG information in practical scenarios. Our experiments show that (1) linearized triples are more effective than fluent NL text in helping LLMs understand KG information and answer fact-intensive questions; (2) Different LLMs exhibit varying preferences for different organizational formats of triples; (3) LLMs with larger scales are more susceptible to noisy, incomplete subgraphs.
Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）
Abstraction-of-Thought Makes Language Models Better Reasoners
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能学院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tencent AI Lab（腾讯AI实验室）
Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness
School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（复杂系统认知与决策智能重点实验室，中国科学院自动化研究所）；China Merchants Bank（中国民生银行）
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments
School of Information Science and Technology, VISTEC（信息科学与技术学院，VISTEC）；KAIST（韩国科学技术院）；Cohere；SCB 10X；AI Singapore（AI新加坡）；Department of Computer Engineering, Chulalongkorn University（朱拉隆梭大学计算机工程系）
Understanding Reasoning in Chain-of-Thought from the Hopfieldian View
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Soochow University（苏州大学）；University of Southampton（南安普顿大学）；Arizona State University（亚利桑那州立大学）
Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology（哈尔滨工业大学社会计算与交互机器人研究中心）；Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳研究院计算与智能研究所）；Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center, Guizhou University（贵州大学文字计算与认知智能教育部工程研究中心）；Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Peking University（北京大学）；ByteDance Seed (China)（字节跳动种子（中国））
ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Research Center for SCIR, Harbin Institute of Technology（SCIR研究中心，哈尔滨工业大学）；NExT Research Center, National University of Singapore（NExT研究中心，新加坡国立大学）
ThoughtSource: A central hub for large language model reasoning data
Medical University of Vienna（维也纳医科大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital（哥本哈根大学医院）；The Royal Institute of Technology (KTH)（皇家理工学院（KTH））
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning
School of Computer Science, Wuhan University（武汉大学计算机学院）；National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Electronic Information Engineering, Henan Polytechnic Institute（河南工业职业技术学院电子信息工程学院）；JD Explore Academy（京东探索研究院）
Thought Graph: Generating Thought Process for Biological Reasoning
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；California Institute of Technology（加利福尼亚理工学院）
Better Accuracies, Worse Reasoning: A Step-Level Audit of Medical Chain-of-Thought Distillation
School of Health & Wellbeing, University of Glasgow（健康与福祉学院，格拉斯哥大学）；Department of Respiratory and Critical Care Medicine, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine（呼吸与危重医学科，上海第六人民医院，上海交通大学医学院）；School of Life Science and Technology, University of Electronic Science and Technology of China（生命科学与技术学院，电子科技大学）；Institute of Health Informatics, University College London（健康信息学研究所，伦敦大学学院）
