Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection
University of Pittsburgh（匹兹堡大学）；University of Maryland Eastern Shore（马里兰大学东海岸分校）；Miami University（迈阿密大学）；San Francisco Bay University（旧金山湾大学）；Boston University（波士顿大学）；Northern Arizona University（北亚利桑那大学）
Ye Zhang, Qian Leng, Mengran Zhu, Rui Ding, Yue Wu, Jintong Song, Yulu Gong
The rapid advancement of Large Language Models (LLMs) has ushered in an era where AI-generated text is increasingly indistinguishable from human-generated content. Detecting AI-generated text has become imperative to combat misinformation, ensure content authenticity, and safeguard against malicious uses of AI. In this paper, we propose a novel hybrid approach that combines traditional TF-IDF techniques with advanced machine learning models, including Bayesian classifiers, Stochastic Gradient Descent (SGD), Categorical Gradient Boosting (CatBoost), and 12 instances of Deberta-v3-large models. Our approach aims to address the challenges associated with detecting AI-generated text by leveraging the strengths of both traditional feature extraction methods and state-of-the-art deep learning models. Through extensive experiments on a comprehensive dataset, we demonstrate the effectiveness of our proposed method in accurately distinguishing between human and AI-generated text. Our approach achieves superior performance compared to existing methods. This research contributes to the advancement of AI-generated text detection techniques and lays the foundation for developing robust solutions to mitigate the challenges posed by AI-generated content.
HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization
School of Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；Huawei Technologies Co., Ltd（华为技术有限公司）
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
School of Informatics, Xiamen University（厦门大学信息学院）；Tsinghua University（清华大学）；Xiamen Unisound Intelligence Technology Co., Ltd（厦门Unisound智能科技有限公司）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），中华人民共和国文化和旅游部，中国）
Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
School of Computer Science, University of Nottingham, Nottingham, United Kingdom（诺丁汉大学计算机科学学院）；School of Informatics and Computing, Northern Arizona University, Flagstaff, United States of America（北亚利桑那大学信息学院）；School of Computer Science, University of Nottingham, Ningbo, China（诺丁汉大学宁波校区）
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration
Digital hybridity and relics in cultural heritage: using corpus linguistics to inform design in emerging technologies from AI to VR
School of English, University of Nottingham（诺丁汉大学英语学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Department of Modern Languages, Aberystwyth University（阿伯里斯特威斯大学现代语言系）
LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
