Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that require accuracy and dependability. In this paper, we introduce a reliable and high-speed production system aimed at detecting and rectifying the hallucination issue within LLMs. Our system encompasses named entity recognition (NER), natural language inference (NLI), span-based detection (SBD), and an intricate decision tree-based process to reliably detect a wide range of hallucinations in LLM responses. Furthermore, we have crafted a rewriting mechanism that maintains an optimal mix of precision, response time, and cost-effectiveness. We detail the core elements of our framework and underscore the paramount challenges tied to response time, availability, and performance metrics, which are crucial for real-world deployment of these technologies. Our extensive evaluation, utilizing offline data and live production traffic, confirms the efficacy of our proposed framework and service.
FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
Department of Computer Science, University of Texas at Dallas（德克萨斯大学达拉斯分校计算机科学系）；Department of Electrical Engineering Computer Science, University of Michigen（密歇根大学电子工程与计算机科学系）
Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective
Gaoling School of Artificial Intelligence（北京理工大学人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation（下一代智能搜索与推荐工程研究中心）
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）
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
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）
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）
Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization
Dept. of Comp. Sci. and Tech.（计算机科学与技术系）；Institute for AI（人工智能研究院）；Tsinghua-Bosch Joint ML Center（清华大学-博世联合机器学习中心）；THBI Lab（THBI实验室）；BNRist Center（BNRist中心）；Tsinghua University（清华大学）
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）
Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer
Technion – Israel Institute of Technology（技术ion-以色列理工学院）；University of Oxford and WhiteBox（牛津大学和WhiteBox）；School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）
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）
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；DIRO, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；School of Information, Renmin University of China（中国人民大学信息资源管理学院）
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）
University of Oxford（牛津大学）；Rural Senses Ltd.（Rural Senses有限公司）；University of Cambridge（剑桥大学）
Most well-established data collection methods currently adopted in NLP depend on the assumption of speaker literacy. Consequently, the collected corpora largely fail to represent swathes of the global population, which tend to be some of the most vulnerable and marginalised people in society, and often live in rural developing areas. Such underrepresented groups are thus not only ignored when making modeling and system design decisions, but also prevented from benefiting from development outcomes achieved through data-driven NLP. This paper aims to address the under-representation of illiterat…
