While Large Language Models have transformed how we interact with AI systems, they suffer from a critical flaw: they confidently generate false information that sounds entirely plausible. This hallucination problem has become a major barrier to deploying these models in real-world applications where accuracy matters. We developed a fact verification framework that catches and corrects these errors in real-time by cross checking LLM outputs against multiple knowledge sources. Our system combines structured databases, live web searches, and academic literature to verify factual claims as they're generated. When we detect inconsistencies, we automatically correct them while preserving the natural flow of the response. Testing across various domains showed we could reduce hallucinations by 67% without sacrificing response quality. Domain experts in healthcare, finance, and scientific research rated our corrected outputs 89% satisfactory a significant improvement over unverified LLM responses. This work offers a practical solution for making LLMs more trustworthy in applications where getting facts wrong isn't an option.
Do I Really Know? Learning Factual Self-Verification for Hallucination Reduction
Qatar Computing Research Institute (QCRI), Hamad Bin Khalifa University (HBKU), Doha, Qatar. \,The authors contributed equally. \,The authors jointly supervised the work
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）；Institute of Trustworthy Embodied AI, Fudan University, Shanghai, China（复旦大学可信具身人工智能研究院）
Robust Multimodal Large Language Models Against Modality Conflict
School of Artificial Intelligence（人工智能学院）；Data Science, University of Science（数据科学，科学大学）；Department of Electronic Engineering（电子工程系）；Information Science, University of Science（信息科学，科学大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
Institute for Clarity in Documentation（文献清晰研究所）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康校区）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models
Tsinghua University, Tencent（清华大学、腾讯）；Tencent（腾讯）；Tencent, University of Macau（腾讯、澳门大学）；University of Science and Technology Beijing（北京科技大学）；Tsinghua University（清华大学）
Cross-Modal Attention Calibration for LVLM Hallucination Mitigation
Sun Yat-sen University（中山大学）；The University of Hong Kong（香港大学）；Meituan（美团）；Inspur Database Technology（Inspur数据库技术）；Guilin University of Electronic Technology（桂林电子科技大学）；Shenzhen Loop Area Institute（深圳河套学院）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东大数据分析与处理重点实验室）
Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Gaoling School of Artifical Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
Hong Kong Baptist University（香港浸会大学）；UC Santa Barbara（加州大学圣巴巴拉分校）；UC Berkeley（加州大学伯克利分校）；Alibaba Group（阿里巴巴集团）；The University of Tokyo（东京大学）；University of Alberta（阿尔伯塔大学）
MHSA: A Lightweight Framework for Mitigating Hallucinations via Steered Attention in LVLMs
Tsinghua University（清华大学）；Tsinghua University, Tencent（清华大学腾讯）；Tencent（腾讯）；University of Science and Technology Beijing（北京科技大学）；University of Macau（澳门大学）
GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
National Center of Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH)（美国国立卫生研究院国家癌症研究所癌症研究中心癌症生物学与遗传学实验室）
OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
The University of Manchester（曼彻斯特大学）；The University of Hong Kong（香港大学）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Academy of Educational Sciences（上海市教育科学研究院）；National University of Singapore（新加坡国立大学）
Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction
Huazhong University of Science and Technology, School of Electronic Information and Communications（华中科技大学电子信息与通信学院）；MoE Key Lab of Artificial Intelligence, AI Institute, X-LANCE Lab, Shanghai Jiao Tong University, Shanghai, China（人工智能联合实验室、AI研究院、X-LANCE实验室、上海交通大学）；AISpeech Ltd, Suzhou, China（上海苏州AISpeech有限公司）
AI Hallucination from Students' Perspective: A Thematic Analysis
Department of Computer and Information Engineering, Center for Cyber Physical Systems, Khalifa University（计算机与信息工程系，跨物理系统中心，哈利法大学）；Department of Psychology, University Malaysia Sabah（心理学系，马来西亚Sabah大学）
Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
Computational Unit, Z.H. College of Engineering & Technology, Aligarh Muslim University, India（计算单元，Z.H. 工程技术学院，阿利加尔穆斯林大学，印度）；Interdisciplinary Center for Artificial Intelligence, Aligarh Muslim University, India（跨学科人工智能中心，阿利加尔穆斯林大学，印度）；Department of Computer Engineering, Aligarh Muslim University, India（计算机工程系，阿利加尔穆斯林大学，印度）
