A preliminary version of this work appeared in the Complex Networks 2024 conference, under the title "LLMs hallucinate graphs too: a structural perspective"
Large Language Models (LLMs) are nowadays prompted for a wide variety of tasks. In this article, we investigate their ability in reciting and generating graphs. We first study the ability of LLMs to regurgitate well known graphs from the literature (e.g. Karate club or the graph atlas)4. Secondly, we question the generative capabilities of LLMs by asking for Erdos-Renyi random graphs. As opposed to the possibility that they could memorize some Erdos-Renyi graphs included in their scraped training set, this second investigation aims at studying a possible emergent property of LLMs. For both tasks, we propose a metric to assess their errors with the lens of hallucination (i.e. incorrect information returned as facts). We most notably find that the amplitude of graph hallucinations can characterize the superiority of some LLMs. Indeed, for the recitation task, we observe that graph hallucinations correlate with the Hallucination Leaderboard, a hallucination rank that leverages 10, 000 times more prompts to obtain its ranking. For the generation task, we find surprisingly good and reproducible results in most of LLMs. We believe this to constitute a starting point for more in-depth studies of this emergent capability and a challenging benchmark for their improvements. Altogether, these two aspects of LLMs capabilities bridge a gap between the network science and machine learning communities.
Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability
U2IS, ENSTA, Institut Polytechnique de Paris（巴黎综合理工学院ENSTA学院U2IS实验室）；ISIR, Université Sorbonne, Pierre et Marie Curie（索邦大学皮埃尔和玛丽·居里分校ISIR实验室）；AMIAD, Pôle Recherche（AMIAD研究部）
Hallucinations in Large Multilingual Translation Models
Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；University of Edinburgh（爱丁堡大学）；CentraleSupélec, Université Paris-Saclay（巴黎萨克雷大学中央理工-高等电力学院）；Unbabel（Unbabel公司）
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
Xi’an Jiaotong University（西安交通大学）；National Engineering Laboratory for Big Data Analytics（国家大数据分析工程实验室）；Nanyang Technological University（南洋理工大学）；Lenovo Research（联想研究院）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Zhejiang University of Technology（浙江工业大学）
RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems
Hong Kong Polytechnic University（香港理工大学）；New York University Shanghai（纽约大学上海分校）；Hong Kong University of Science and Technology（香港科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models
City University of Hong Kong（香港城市大学）；Northwestern Polytechnical University（西北工业大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
Align Generative Artificial Intelligence with Human Preferences: A Novel Large Language Model Fine-Tuning Method for Online Review Management
Department of Information Systems and Operations Management, The University of Texas at Arlington（信息系统与运营管理系，德克萨斯大学阿灵顿分校）；Department of Management Information Systems, University of Arizona（管理信息学系，亚利桑那大学）
BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali
Department of Computer Science and Engineering, Islamic University of Technology（伊斯兰科技大学计算机科学与工程系）；Department of Computer Science and Engineering, University of California（加州大学计算机科学与工程系）
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）
Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
Ministry of Education Key Laboratory of Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research（前沿人工智能研究中心，高性能计算研究所，科技研究局）
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）
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）
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）
