The rapid spread of fake news presents a significant global challenge, particularly in low-resource languages like Bangla, which lack adequate datasets and detection tools. Although manual fact-checking is accurate, it is expensive and slow to prevent the dissemination of fake news. Addressing this gap, we introduce BanFakeNews-2.0, a robust dataset to enhance Bangla fake news detection. This version includes 11,700 additional, meticulously curated fake news articles validated from credible sources, creating a proportional dataset of 47,000 authentic and 13,000 fake news items across 13 categories. In addition, we created a manually curated independent test set of 460 fake and 540 authentic news items for rigorous evaluation. We invest efforts in collecting fake news from credible sources and manually verified while preserving the linguistic richness. We develop a benchmark system utilizing transformer-based architectures, including fine-tuned Bidirectional Encoder Representations from Transformers variants (F1-87\%) and Large Language Models with Quantized Low-Rank Approximation (F1-89\%), that significantly outperforms traditional methods. BanFakeNews-2.0 offers a valuable resource to advance research and application in fake news detection for low-resourced languages. We publicly release our dataset and model on Github to foster research in this direction.
Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection
School of Cyber Science and Technology, Beihang University（信息科学与技术学院，北京航空航天大学）；School of Cyber Science and Technology, Beijing University of Posts and Telecommunications（信息科学与技术学院，北京邮电大学）；Department of Computer Science, University of Illinois at Chicago（计算机科学系，伊利诺伊大学芝加哥分校）
Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
University of Science and Technology of China（中国科学技术大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；The Hong Kong University of Science and Technology（香港科技大学）
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection
Dept. of CSE IIT Kharagpur, India（印度 IIT 卡哈拉格普尔分校计算机科学与工程系）；Dept. of CSE IIIT Delhi, India（印度德里信息技术学院计算机科学与工程系）；IBM Research Bangalore, India（印度班加罗尔 IBM 研究院）
ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
Department of Computer Science and Engineering, University of Notre Dame（诺丁汉大学计算机科学与工程系）；MIT（麻省理工学院）；CalTech（加州理工学院）；MBZUAI（穆斯林人工智能研究所）；CMU（卡内基梅隆大学）；Department of Chemistry & Biochemistry, University of Notre Dame（诺丁汉大学化学与生物化学系）
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation
Southern University of Science and Technology（南方科技大学）；Microsoft Research Asia（微软亚洲研究院）；Shanghai University of Finance and Economics（上海财经大学）；Peking University（北京大学）
Unified Large Language Models for Misinformation Detection in Low-Resource Linguistic Settings
College of Science and Engineering（科学与工程学院）；Department of Computer Science（计算机科学系）；University of Hertfordshire（赫特福德郡大学）；Applied College, King Khalid University（国王·卡利德大学应用学院）；Faculty of Resilience（韧性学院）；Faculty of Computer Science（计算机科学系）
Cross-Linguistic Persona-Driven Data Synthesis for Robust Multimodal Cognitive Decline Detection
Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Informatics, Nanjing Medical University（智能诊疗技术与仪器工程研究中心、教育部、生物医学工程与信息学院、南京医科大学）；Institute of Biomedical Engineering, University of Oxford（生物医学工程研究所、牛津大学）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（高性能计算研究所、科技研究局（A*STAR））；Department of Neurology, National Neuroscience Institute, Singapore, 308433, Singapore（神经病学部、新加坡国家神经科学研究所）；Duke-NUS Medical School, Singapore, 169857, Singapore（新加坡国立大学医学院）
