The emergence of Large Language Models (LLMs) has revolutionized how users access information, shifting from traditional search engines to direct question-and-answer interactions with LLMs. However, the widespread adoption of LLMs has revealed a significant challenge known as hallucination, wherein LLMs generate coherent yet factually inaccurate responses. This hallucination phenomenon has led to users' distrust in information retrieval systems based on LLMs. To tackle this challenge, this paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in LLMs. DRAD improves upon traditional retrieval augmentation by dynamically adapting the retrieval process based on real-time hallucination detection. It features two main components: Real-time Hallucination Detection (RHD) for identifying potential hallucinations without external models, and Self-correction based on External Knowledge (SEK) for correcting these errors using external knowledge. Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs. All of our code and data are open-sourced at https://github.com/oneal2000/EntityHallucination.
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；State Key Laboratory of Computer Science（计算机科学国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；School of Public Health, Wuhan University（武汉大学公共卫生学院）；Insight Centre for Data Analytics, University of Galway（Galway大学数据分析研究中心）；Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据分析研究中心）
Exploring and Mitigating Fawning Hallucinations in Large Language Models
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳MSU-BIT大学人工智能研究院）；Tianjin University（天津大学）；The Hong Kong University of Science（香港科学大学）
Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明负责任的人工智能与数据分析实验室）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；University of Electronic Science and Technology of China（中国电子科学技术大学）；University of Copenhagen（哥本哈根大学）
NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning
Central South University（中南大学）；Tsinghua University（清华大学）；Nanjing University（南京大学）；Suzhou Aerospace Information Research Institute（苏州空天信息研究院）；McGill University（麦吉尔大学）
Mitigating Object Hallucination via Robust Local Perception Search
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China（未来媒体中心及电子科技大学计算机科学与工程学院）
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Hunan Normal University（湖南师范大学）
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子信息学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
