Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Salesforce Research Asia（Salesforce 亚洲研究院）；State Street Technology (Zhejiang) Ltd.（道富科技（浙江）有限公司）
Qiheng Mao, Zemin Liu, Chenghao Liu, Zhuo Li, Jianling Sun
The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL. Despite a growing body of research dedicated to integrating LLMs into the graph domain, a comprehensive review that deeply analyzes the core components and operations within these models is notably lacking. Our survey fills this gap by proposing a novel taxonomy that breaks down these models into primary components and operation techniques from a novel technical perspective. We further dissect recent literature into two primary components including knowledge extractors and organizers, and two operation techniques including integration and training stratigies, shedding light on effective model design and training strategies. Additionally, we identify and explore potential future research avenues in this nascent yet underexplored field, proposing paths for continued progress.
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Al-Ahliyya Amman University（阿赫利耶阿曼大学）；Queensland University of Technology（昆士兰科技大学）
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
State Key Lab of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Computing and Mathematical Sciences, Birkbeck, University of London（伦敦大学伯克贝克学院计算与数学科学学院）
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
FPT Software AI Center（FPT软件人工智能中心）；Hanoi University of Science and Technology（河内科技大学）；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Fulbright University（富布赖特大学）
本文提出多语言代码-文本数据集 The Vault，包含 4300 万高质量样本，通过规则与深度学习方法提取构建，实验表明在其上微调的代码大模型在代码生成、搜索和摘要任务上优于 CodeSearchNet 等数据集训练结果。
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models
Foundation Models Lab, BKAI（BKAI基础模型实验室）；Hanoi University of Science and Technology（河内科技大学）；University of Information Technology（信息技术大学）；Vietnam National University HCMC（胡志明市越南国家大学）
A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models
Amirkabir University of Technology（阿米尔卡比尔理工大学）；Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；Amazon GenAI（亚马逊生成式AI部门）
A Comprehensive Survey on Aspect Based Sentiment Analysis
Vellore Institute Of Technology（韦洛尔理工大学）；School of Computer Science and Engineering- SCOPE（计算机科学与工程学院-SCOPE）
基于方面的情感分析（Aspect Based Sentiment Analysis，ABSA）是自然语言处理的一个分支，它主要是将数据分成方面，并最终提取情感信息。已知ABSA比一般的情感分析提供更多关于上下文的信息。在这项研究中，我们的目的是探索各种方法，同时执行ABSA，并提供一个比较研究。这份调查报告深入讨论了各种解决方案，并对它们进行了比较。并方便地分为几个部分，以获得对该过程的整体看法。
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Concordia University（康考迪亚大学）；Aqaba University of Technology（亚喀巴科技大学）；Khalifa University（哈利法大学）；University of Alberta（阿尔伯塔大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Istinye University（伊斯蒂涅大学）；University of Waterloo（滑铁卢大学）
GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling
UCEK, JNTU Kakinada（贾夫纳大学工程学院，贾夫纳印度理工学院卡基纳达分校）；Brane Enterprises（布兰恩企业）；School of EECS, College of Engineering and Mines, University of North Dakota（北达科他大学工程与矿业学院电子工程与计算机科学学院）
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques
Artificial Intelligence and Innovation Centre, University of Kurdistan Hewler（库尔德斯坦胡勒尔大学人工智能与创新中心）；Computer Science and Engineering Department, School of Science and Engineering, University of Kurdistan Hewler（库尔德斯坦胡勒尔大学科学与工程学院计算机科学与工程系）；School of Computing and Engineering, University of Huddersfield（赫德斯菲尔德大学计算与工程学院）；Department of Computer Science, College of Science, Charmo University（查莫大学科学学院计算机科学系）；Software and Informatics Engineering, College of Engineering, Salahaddin University-Erbil（萨拉赫丁大学埃尔比尔工程学院软件与信息技术工程）
