Survey of Hallucination in Natural Language Generation
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；Hong Kong University of Science and Technology（香港科技大学）
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Wenliang Dai, Ho Shu Chan, Andrea Madotto, Pascale Fung
近年来，得益于基于Transformer的语言模型等序列到序列深度学习技术的发展，自然语言生成（Natural Language Generation, NLG）技术取得了指数级进步。这一进展让NLG生成的文本更流畅连贯，推动了摘要生成、对话生成、数据到文本生成等下游任务的发展。然而，基于深度学习的生成模型显然容易产生非预期的幻觉文本，这会降低系统性能，在许多实际场景中无法满足用户预期。为解决这一问题，已有大量研究围绕幻觉文本的度量与缓解展开，但此前从未有研究对这些工作进行全面梳理。因此，本综述对NLG中幻觉问题的研究进展与挑战进行了广泛概述。本综述分为三部分：（1）对度量指标、缓解方法及未来方向的总体概述；（2）针对以下下游任务中幻觉问题的特定任务研究进展概述，包括摘要生成、对话生成、生成式问答、数据到文本生成、机器翻译以及视觉-语言生成；（3）大语言模型（Large Language Models, LLMs）中的幻觉问题。本综述旨在促进研究者协同努力，应对NLG中的幻觉文本挑战。
英文摘要：
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation; and (3) hallucinations in large language models (LLMs). This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Shihezi University（石河子大学）；Beihang University（北航）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Jilin University（吉林大学）；Central South University（中南大学）；Jinan University（暨南大学）
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）
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Tencent AI lab（腾讯AI实验室）；Soochow University（苏州大学）；Zhejiang University（浙江大学）；Renmin University of China（中国人民大学）；Nanyang Technological University（南洋理工大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
An Audit on the Perspectives and Challenges of Hallucinations in NLP
College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）；Department of Computer Science & Engineering, Pennsylvania State University（宾夕法尼亚州立大学计算机科学与工程系）；Adobe Research（奥多比研究中心）
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）；Hugging Face；Department of Computer Science, University College London（伦敦大学学院计算机系）；Together AI
Hallucination Mitigating for Medical Report Generation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）；The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education（教育部脑机智能技术重点实验室）
AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油和矿物大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination
Biomedical Informatics and Medical Education（生物医学信息学与医学教育）；Health AI（健康人工智能）；Department of Information Sciences and Technology（信息科学与技术系）；Department of Linguistics（语言学系）
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）
HalluScore: Large Language Model Hallucination Question Answering Benchmark
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学信息与计算机科学系）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
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）
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
Institute for Advanced Algorithms Research（高级算法研究院）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；School of Information, Renmin University of China（中国人民大学信息学院）
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
