大型语言模型（LLMs）在语言理解和生成方面表现出色，但容易产生幻觉，输出事实错误或缺乏依据的内容。检索增强生成（RAG）系统通过用外部知识为LLM响应提供依据来解决这一问题。本研究使用三种检索方法评估了检索器有效性与LLM幻觉减少之间的关系：基于BM25关键词搜索的稀疏检索、使用Sentence Transformers进行语义搜索的密集检索，以及一个提出的混合检索模块。该混合模块结合了查询扩展，并通过动态加权的倒数排名融合（Reciprocal Rank Fusion）分数合并稀疏检索器和密集检索器的结果。使用HaluBench数据集（一个用于问答任务中幻觉评估的基准），我们采用平均精度均值（mean average precision）和归一化折损累计增益（normalised discounted cumulative gain）等指标评估检索性能，重点关注前三个检索文档的相关性。结果表明，混合检索器取得了更好的相关性分数，优于稀疏检索器和密集检索器。进一步使用准确率、幻觉率和拒绝率等指标，将LLM生成的答案与真实答案进行对比评估后发现，混合检索器在失败案例上取得了最高准确率、最低幻觉率和最低拒绝率。这些发现凸显了混合检索器在增强检索相关性、降低幻觉率以及提高LLM可靠性方面的能力，强调了先进检索技术在缓解幻觉和提高响应准确性方面的重要性。
英文摘要：
Large Language Models (LLMs) excel in language comprehension and generation but are prone to hallucinations, producing factually incorrect or unsupported outputs. Retrieval Augmented Generation (RAG) systems address this issue by grounding LLM responses with external knowledge. This study evaluates the relationship between retriever effectiveness and hallucination reduction in LLMs using three retrieval approaches: sparse retrieval based on BM25 keyword search, dense retrieval using semantic search with Sentence Transformers, and a proposed hybrid retrieval module. The hybrid module incorporates query expansion and combines the results of sparse and dense retrievers through a dynamically weighted Reciprocal Rank Fusion score. Using the HaluBench dataset, a benchmark for hallucinations in question answering tasks, we assess retrieval performance with metrics such as mean average precision and normalised discounted cumulative gain, focusing on the relevance of the top three retrieved documents. Results show that the hybrid retriever achieves better relevance scores, outperforming both sparse and dense retrievers. Further evaluation of LLM-generated answers against ground truth using metrics such as accuracy, hallucination rate, and rejection rate reveals that the hybrid retriever achieves the highest accuracy on fails, the lowest hallucination rate, and the lowest rejection rate. These findings highlight the hybrid retriever's ability to enhance retrieval relevance, reduce hallucination rates, and improve LLM reliability, emphasising the importance of advanced retrieval techniques in mitigating hallucinations and improving response accuracy.
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）
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Stanford University（斯坦福大学）；Penn State University（宾夕法尼亚州立大学）；Hong Kong University of Science and Technology（香港科技大学）
A Concise Review of Hallucinations in LLMs and their Mitigation
Computer Engineering K J Somaiya Institute of Technology Mumbai, India；Data Science K J Somaiya Institute of Technology Mumbai, India；Information Technology K J Somaiya Institute of Technology Mumbai, India
An Evolutionary Large Language Model for Hallucination Mitigation
BIOSTIM Laboratory Faculty of Medicine Salah Boubnider University Constantine, Algeria（BIOSTIM实验室医学院萨拉赫·布宾德大学阿尔及利亚科纳克特）；Department of IFA Faculty of NTIC Abdelhamid Mehri University Constantine, Algeria（IFA部门NTIC学院阿卜杜勒哈米德·梅赫里大学阿尔及利亚科纳克特）
Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
College of Computer Science, Nankai University（南开大学计算机科学学院）；Haihe Lab of ITAI College of intelligent Science and Technology, Inner Mongolia University of Technology（内蒙古工业大学智能科学与技术学院海河实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Department of Electrical and Computer Engineering, Stony Brook University（石溪大学电气与计算机工程系）；Department of Computer Science, Michigan Technological University（密歇根技术大学计算机科学系）
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）
HAVE: Head-Adaptive Gating and ValuE Calibration for Hallucination Mitigation in Large Language Models
Xin Tong School of Information and Cyber Security People’s Public Security University of China（信息与网络安全学院 中国人民公安大学）；Zhi Lin School of Safety Science Tsinghua University（安全科学学院 清华大学）；Jingya Wang School of Information and Cyber Security People’s Public Security University of China（信息与网络安全学院 中国人民公安大学）；Bo Jin* The Third Research Institute of the Ministry of Public Security of China（公安部第三研究所）
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所信息处理实验室，北京，中国）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）；Xiaohongshu Inc（小红书公司）
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University)（东南大学新一代人工智能技术及其交叉应用关键实验室）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Wuhan University of Technology（武汉理工大学）；National University of Singapore（新加坡国立大学）
Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Institute of Technology（北京理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）
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）
HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents
MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences（自动化研究所人工智能与自然语言处理实验室，中国科学院）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Meituan（美团）
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；Southwest Jiaotong University（西南交通大学）；Shanghai Jiaotong University（上海交通大学）；Chinese University of Hong Kong（香港中文大学）
RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation
College of Computer Science, Chongqing University（重庆大学计算机科学学院）；Heavy Rainfall Research Center of China（中国强降水研究中心）；CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences（中国气象局气候资源转化经济重点开放实验室、重庆气象科学研究院）；Chongqing Metropolitan College of Science（重庆科学理工学院）；School of Intelligence Science（智能科学学院）；College of Artificial Intelligence, Harbin Institute of Technology（人工智能学院、哈尔滨工业大学）；BNU-UIC Institute of Artificial Intelligence（北京师范大学-国际学院人工智能与未来网络研究院）；Future Networks, Beijing Normal University at Zhuhai（未来网络、北京师范大学珠海分校）
